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Date: Thu, 16 Jul 2026 02:04:32 +0000
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Update major vendor entries (NVIDIA Rubin/Blackwell, AMD MI350, Intel Gaudi 3,
Google TPU Trillium/Ironwood, AWS Trainium2, Azure Maia, Meta MTIA, Cerebras WSE-3,
Groq/NVIDIA), refresh compilers and MLPerf v6.0 links, and fix asset/header hygiene.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md | 380 +++++++++++++++++++++++++++++++-----------------------
 1 file changed, 218 insertions(+), 162 deletions(-)

diff --git a/README.md b/README.md
index 15290b2..25b3833 100644
--- a/README.md
+++ b/README.md
@@ -1,63 +1,32 @@
 <div align="center"><h1>AI Chip (ICs and IPs)</h1></div>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/AI-chips.png"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI-chips.png"></div>
 <br>
-<div align="center">Editor <a href="https://www.linkedin.com/in/shan-tang-27342510/"><strong>S.T.</strong></a>(Linkedin)</div>
-<div align="center"><strong>Welcome to My Wechat Blog <a href="[https://mp.weixin.qq.com/s/axfIBbQBDhTJ2Zt7U5WQBw](https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&__biz=MzI3MDQ2MjA3OA==&scene=1&album_id=1374108991751782402&count=3#wechat_redirect)">StarryHeavensAbove</a> for more AI chip related articles</strong></div>
-<div align="center"><strong>欢迎访问我的微信公众号 <a href="[https://mp.weixin.qq.com/s/axfIBbQBDhTJ2Zt7U5WQBw](https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&__biz=MzI3MDQ2MjA3OA==&scene=1&album_id=1374108991751782402&count=3#wechat_redirect)">StarryHeavensAbove</a></strong></div>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/qrcode_for_weichat_258.jpg" height="100"></div>
+<div align="center">Editor <a href="https://www.linkedin.com/in/shan-tang-27342510/"><strong>S.T.</strong></a> (LinkedIn)</div>
+<div align="center"><strong>Welcome to My WeChat Blog <a href="https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&amp;__biz=MzI3MDQ2MjA3OA==&amp;scene=1&amp;album_id=1374108991751782402&amp;count=3#wechat_redirect">StarryHeavensAbove</a> for more AI chip related articles</strong></div>
+<div align="center"><strong>欢迎访问我的微信公众号 <a href="https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&amp;__biz=MzI3MDQ2MjA3OA==&amp;scene=1&amp;album_id=1374108991751782402&amp;count=3#wechat_redirect">StarryHeavensAbove</a></strong></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/qrcode_for_weichat_258.jpg" height="100"></div>
 <div align="center"><h1> </h1></div>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/AI_Chip_Landscape_v0p7.png"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI_Chip_Landscape_v0p7.png"></div>
 <div align="center"><h1> </h1></div>
  
 <div align="center"><h2>Latest updates</h2></div>
+<p align="center"><em>Last refreshed: July 2026 — major vendor coverage updated for the generative-AI / LLM accelerator era.</em></p>
 <HR>
 
-<font color="Darkred">
 <ul>
-<li>Add news of <a href="#SambaNova">SambaNova</a>.</li>
-<li>Add news of <a href="#Groq">Groq</a>.</li>
-<li>Add news of <a href="#d-matrix">d-Matrix</a>.</li>
-<li>Add news of <a href="#Neureality">Neureality</a>.</li>
-<li>Add news of <a href="#Qualcomm">Qualcomm</a>.</li>
-<li>Add news of <a href="#Nvidia">Nvidia</a>.</li>
-<li>Add news of <a href="#Cerebras">Cerebras</a>.</li>
-<li>Add link to <a href="#AIChipBenchmarks">Latest MLPerf Results from MLCommons</a>.</li>
-<li>Add news of <a href="#IBM">IBM AIU</a>.</li>
-<li>Add news of <a href="#Tesla">Tesla Dojo</a>.</li>
-<li>Add link to <a href="#AIChipBenchmarks">Latest MLPerf Results from MLCommons</a>.</li>
-<li>Add news of <a href="#Cerebras">Cerebras</a>.</li>
-<li>Add startup <a href="#d-matrix">d-Matrix</a>.</li>
-<li>Add news of <a href="#Tachyum">Tachyum Prodigy Universal Processor</a>.</li>
-<li>Add news of <a href="#Habana">Intel Habana Gaudi®2</a>.</li>
-<li>Add startup <a href="#Modular">Modular AI in AI compiler section</a>.</li>
-<li>Add startup <a href="#Teramem">TeraMem</a>.</li>
-<li>Add startup <a href="#Aspinity">Aspinity</a>.</li>
-<li>Add news of <a href="#Synopsys">Synopsys DesignWare ARC NPX6 NPU IP</a>.</li>
-<li>Add news of <a href="#Nvidia">Nvidia Hopper</a>.</li>
-<li>Add news of <a href="#Graphcore">Graphcore</a>.</li>
-<li>Add startup <a href="#Ceremorphic">Ceremorphic</a>.</li>
-<li>Add news of <a href="#Lightelligence">Lightelligence</a>.</li>
-<li>Add link to <a href="#AIChipBenchmarks">Latest MLPerf Results from MLCommons</a>.</li>
-<li>Add news of <a href="#Cerebras">Cerebras</a>.</li>
-<li>Add news of <a href="#Habana">Habana</a>.</li>
-<li>Add news of <a href="#Google">Google Tensor Chip</a>.</li>
-<li>Add news of <a href="#Intel">Intel Loihi 2</a>.</li>
-<li>Add news of <a href="#Tesla">Tesla Dojo</a>.</li>
-<li>Add news of <a href="#Untether">Untether AI</a>.</li>
-<li>Add startup <a href="#Innatera">Innatera Nanosystems</a>.</li>
-<li>Add startup <a href="#EdgeQ">EdgeQ</a>.</li>
-<li>Add startup <a href="#Quadric">Quadric</a>.</li>
-<li>Add startup <a href="#AnalogInference">Analog Inference</a>.</li>
-<li>Add news of <a href="#Tenstorrent">Tenstorrent</a>.</li>
-<li>Add news of <a href="#Google">Google</a>.</li>
-<li>Add news of <a href="#SiMa">SiMa.ai</a>.</li>
-<li>Add startup <a href="#Neureality">Neureality</a>.</li>
-<li>Add news of <a href="#Cerebras">Cerebras</a>.</li>
-<li>Add news of <a href="#Groq">Groq</a>.</li>
-<li>Add news of <a href="#Nvidia">Nvidia</a>.</li>
-<li>Add news of <a href="#SambaNova">SambaNova</a>.</li>
+<li><strong>NVIDIA</strong>: <a href="#Nvidia">Vera Rubin</a> platform (CES/GTC 2026) and <a href="#Nvidia">Blackwell</a> (B200 / GB200 NVL72).</li>
+<li><strong>AMD</strong>: <a href="#AMD">Instinct MI350</a> series (CDNA 4) and Meta partnership for Helios / MI450-class GPUs.</li>
+<li><strong>Intel</strong>: <a href="#Habana">Gaudi 3</a> AI accelerator for enterprise GenAI training and inference.</li>
+<li><strong>Google</strong>: Cloud TPU <a href="#Google">Trillium (v6e)</a> and <a href="#Google">Ironwood (TPU7x)</a>.</li>
+<li><strong>Amazon AWS</strong>: <a href="#Amazon_AWS">Trainium2</a> / Trn2 UltraServers and Inferentia2.</li>
+<li><strong>Microsoft</strong>: <a href="#Microsoft">Azure Maia 100</a> custom AI accelerator.</li>
+<li><strong>Meta</strong>: <a href="#Facebook">MTIA</a> custom training &amp; inference accelerators (multi-generation roadmap).</li>
+<li><strong>Cerebras</strong>: <a href="#Cerebras">WSE-3 / CS-3</a> wafer-scale engine.</li>
+<li><strong>Groq</strong>: LPU inference focus; technology licensed / team joined <a href="#Groq">NVIDIA</a> (late 2025).</li>
+<li><strong>Benchmarks</strong>: Links updated to <a href="#AIChipBenchmarks">MLPerf Inference v6.0</a> and <a href="#AIChipBenchmarks">Training v6.0</a>.</li>
+<li><strong>Compilers</strong>: Refreshed <a href="#AIChipCompilers">AI Chip Compilers</a> list (Triton, IREE, TensorRT-LLM, etc.).</li>
+<li><strong>Repo hygiene</strong>: Fixed asset links to this repository; cleaned header WeChat album links.</li>
 </ul>
-</font>
 
 <div align="center"><h1> </h1></div>
 
@@ -68,14 +37,14 @@
     <th><a href="#IC_Vendors">IC Vendors</a></th><td><a href="#Intel">Intel</a>, <a href="#Qualcomm">Qualcomm</a>, <a href="#Nvidia">Nvidia</a>, <a href="#Samsung">Samsung</a>, <a href="#AMD">AMD</a>,<a href="#IBM">IBM</a>, <a href="#Marvell">Marvell</a></td>
   </tr>
   <tr>
-    <th><a href="#Tech_Giants">Tech Giants & HPC Vendors</a></th><td><a href="#Google">Google</a>, <a href="#Amazon_AWS">Amazon_AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#Alibaba">Alibaba Group</a>, <a href="#Tencent_Cloud">Tencent Cloud</a>, <a href="#Baidu">Baidu</a>, <a href="#Fujitsu">Fujitsu</a>, <a href="#Nokia">Nokia</a>, <a href="#Facebook">Facebook</a>, <a href="#Tesla">Tesla</a></td>
+    <th><a href="#Tech_Giants">Tech Giants & HPC Vendors</a></th><td><a href="#Google">Google</a>, <a href="#Amazon_AWS">Amazon_AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#Alibaba">Alibaba Group</a>, <a href="#Tencent_Cloud">Tencent Cloud</a>, <a href="#Baidu">Baidu</a>, <a href="#Fujitsu">Fujitsu</a>, <a href="#Nokia">Nokia</a>, <a href="#Facebook">Meta (Facebook)</a>, <a href="#Tesla">Tesla</a></td>
   </tr>
   <tr>
     <th><a href="#IP_Vendors">IP Vendors</a></th><td><a href="#ARM">ARM</a>, <a href="#Synopsys">Synopsys</a>, <a href="#Imagination">Imagination</a>, <a href="#CEVA">CEVA</a>, <a href="#Cadence">Cadence</a>, <a href="#VeriSilicon">VeriSilicon</a></td>
   </tr>
   <tr>  
-    <th><a href="#Startups_Worldwide">Startups</a></th>
-    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Blaize">Blaize</a>, <a href="#Koniku">Koniku</a>, <a href="#Adapteva">Adapteva</a>, <a href="#Mythic">Mythic</a>, <a href="#Brainchip">BrainChip</a>, <a href="#Leepmind">Leepmind</a>, <a href="#Groq">Groq</a>, <a href="#Kneron">Kneron</a>, <a href="#Esperanto">Esperanto Technologies</a>, <a href="#GTI">Gyrfalcon Technology</a>, <a href="#SambaNova">SambaNova Systems</a>, <a href="#GreenWaves">GreenWaves Technology</a>, <a href="#Lightelligence">Lightelligence</a>, <a href="#Lightmatter">Lightmatter</a>, <a href="#Hailo">Hailo</a>,<a href="#Tachyum">Tachyum</a>,<a href="#Alphaics">AlphaICs</a>,<a href="#Syntiant">Syntiant</a>, <a href="#aiCTX">aiCTX</a>, <a href="#Flexlogix">Flex Logix</a>, <a href="#PFN">Preferred Network</a>, <a href="#Cornami">Cornami</a>, <a href="#Anaflash">Anaflash</a>, <a href="#Optalysys">Optaylsys</a>, <a href="#etacompute">Eta Compute</a>, <a href="#Achronix">Achronix</a>, <a href="#Areanna">Areanna AI</a>, <a href="#Neuroblade">Neuroblade</a>, <a href="#Luminous">Luminous Computing</a>, <a href="#Efinix">Efinix</a>, <a href="#AIstorm">AISTORM</a>, <a href="#SiMa">SiMa.ai</a>,<a href="#Untether">Untether AI</a>, <a href="#GrAI">GrAI Matter Lab</a>, <a href="#Rain">Rain Neuromorphics</a>, <a href="#ABR">Applied Brain Research</a>, <a href="#Xmos">XMOS</a>, <a href="#DinoplusAI">DinoPlusAI</a>, <a href="#Furiosa">Furiosa AI</a>, <a href="#Perceive">Perceive</a>, <a href="#SimpleMachines">SimpleMachines</a>, <a href="#Neureality">Neureality</a>, <a href="#AnalogInference">Analog Inference</a>, <a href="#Quadric">Quadric</a>, <a href="#EdgeQ">EdgeQ</a>, <a href="#Innatera">Innatera Nanosystems</a>, <a href="#Ceremorphic">Ceremorphic</a>, <a href="#Aspinity">Aspinity</a>, <a href="#Teramem">TeraMem, <a href="#d-matrix">d-Matrix</a></a></td>
+    <th><a href="#Startups">Startups</a></th>
+    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Blaize">Blaize</a>, <a href="#Koniku">Koniku</a>, <a href="#Adapteva">Adapteva</a>, <a href="#Mythic">Mythic</a>, <a href="#Brainchip">BrainChip</a>, <a href="#Leepmind">Leepmind</a>, <a href="#Groq">Groq</a>, <a href="#Kneron">Kneron</a>, <a href="#Esperanto">Esperanto Technologies</a>, <a href="#GTI">Gyrfalcon Technology</a>, <a href="#SambaNova">SambaNova Systems</a>, <a href="#GreenWaves">GreenWaves Technology</a>, <a href="#Lightelligence">Lightelligence</a>, <a href="#Lightmatter">Lightmatter</a>, <a href="#Hailo">Hailo</a>,<a href="#Tachyum">Tachyum</a>,<a href="#Alphaics">AlphaICs</a>,<a href="#Syntiant">Syntiant</a>, <a href="#aiCTX">aiCTX</a>, <a href="#Flexlogix">Flex Logix</a>, <a href="#PFN">Preferred Network</a>, <a href="#Cornami">Cornami</a>, <a href="#Anaflash">Anaflash</a>, <a href="#Optalysys">Optaylsys</a>, <a href="#etacompute">Eta Compute</a>, <a href="#Achronix">Achronix</a>, <a href="#Areanna">Areanna AI</a>, <a href="#Neuroblade">Neuroblade</a>, <a href="#Luminous">Luminous Computing</a>, <a href="#Efinix">Efinix</a>, <a href="#AIstorm">AISTORM</a>, <a href="#SiMa">SiMa.ai</a>,<a href="#Untether">Untether AI</a>, <a href="#GrAI">GrAI Matter Lab</a>, <a href="#Rain">Rain Neuromorphics</a>, <a href="#ABR">Applied Brain Research</a>, <a href="#Xmos">XMOS</a>, <a href="#DinoplusAI">DinoPlusAI</a>, <a href="#Furiosa">Furiosa AI</a>, <a href="#Perceive">Perceive</a>, <a href="#SimpleMachines">SimpleMachines</a>, <a href="#Neureality">Neureality</a>, <a href="#AnalogInference">Analog Inference</a>, <a href="#Quadric">Quadric</a>, <a href="#EdgeQ">EdgeQ</a>, <a href="#Innatera">Innatera Nanosystems</a>, <a href="#Ceremorphic">Ceremorphic</a>, <a href="#Aspinity">Aspinity</a>, <a href="#Teramem">TeraMem</a>, <a href="#d-matrix">d-Matrix</a></td>
   </tr>
 </table>
 
@@ -87,10 +56,20 @@
 
 <div align="center"><h3> </h3></div>
 <a name="Nvidia"></a>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Nvidia_logo.png" height="50"> </div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nvidia_logo.png" height="50"> </div>
 <div align="center"><h3> </h3></div>
 <div align="center"><h3>GPU</h3></div>
 
+<p><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Kicks Off the Next Generation of AI With Rubin</a></strong></p>
+<blockquote>
+  <p>CES 2026 — NVIDIA launched the Rubin platform: Vera CPU, Rubin GPU, NVLink 6 Switch, ConnectX-9, BlueField-4 and Spectrum-6, codesigned for agentic AI and large MoE models. vs. Blackwell, NVIDIA claims up to 10x lower inference token cost and 4x fewer GPUs to train MoE models. Partner availability is targeted for H2 2026.</p>
+</blockquote>
+
+<p><strong><a href="https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing">NVIDIA Blackwell Platform Arrives to Power a New Era of Computing</a></strong></p>
+<blockquote>
+  <p>GTC 2024 — Blackwell succeeds Hopper for trillion-parameter generative AI. The B200 Tensor Core GPU and GB200 Grace Blackwell Superchip power rack-scale GB200 NVL72 systems with fifth-generation NVLink, targeting large-scale training and real-time LLM inference.</p>
+</blockquote>
+
 <p><a href="https://nvidianews.nvidia.com/news/nvidia-microsoft-accelerate-cloud-enterprise-ai">NVIDIA Teams With Microsoft to Build Massive Cloud AI Computer</a></p>
 <blockquote>
   <p>Tens of Thousands of NVIDIA GPUs, NVIDIA Quantum-2 InfiniBand and Full Stack of NVIDIA AI Software Coming to Azure; NVIDIA, Microsoft and Global Enterprises to Use Platform for Rapid, Cost-Effective AI Development and Deployment</p>
@@ -109,7 +88,7 @@
 
 <div align="center"><h3> </h3></div>
 <a name="Intel"></a>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Intel_logo.png" height="60"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Intel_logo.png" height="60"></div>
 <div align="center"><h3> </h3></div>
 
 <a name="Mobileye"></a>
@@ -125,6 +104,11 @@
 
 <p><a name="Habana"></a></p>
 <div align="center"><h3>Habana</h3></div>
+<p><strong><a href="https://newsroom.intel.com/artificial-intelligence/vision-2024-enterprise-ai-gaudi-3-open-systems-strategy">Intel Unleashes Enterprise AI with Gaudi 3</a></strong></p>
+<blockquote>
+  <p>Intel Vision 2024 — Gaudi 3 targets generative AI training and inference with Ethernet scale-out. Intel positions Gaudi 3 for enterprise GenAI price/performance vs. GPU alternatives, with OEM availability through Dell, HPE, Lenovo, Supermicro and cloud partners.</p>
+</blockquote>
+
 <p><strong><a href="https://www.intel.com/content/www/us/en/newsroom/news/vision-2022-habana-gaudi2-greco.html">Intel’s Habana Labs Launches Second-Generation AI Processors for Training and Inferencing</a></strong></p>
 <blockquote>
   <p>Today at Intel Vision, Intel announced that Habana Labs, its data center team focused on AI deep learning processor technologies, launched its second-generation deep learning processors for training and inference: Habana® Gaudi®2 and Habana® Greco™. These new processors address an industry gap by providing customers with high-performance, high-efficiency deep learning compute choices for both training workloads and inference deployments in the data center while lowering the AI barrier to entry for companies of all sizes.</p>
@@ -137,9 +121,14 @@
 
 <div align="center"><h3> </h3></div>
 <a name="Qualcomm"></a>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Qualcomm_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Qualcomm_logo.png" height="40"></div>
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Qualcomm Cloud AI 100 / Hexagon NPU</a></strong></p>
+<blockquote>
+  <p>Qualcomm continues to push on-device and edge/cloud AI via Snapdragon platforms (Hexagon NPU) and Cloud AI accelerators for efficient inference outside traditional GPU clusters.</p>
+</blockquote>
+
 <a href="https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/karlfreund/2022/11/16/qualcomm-ups-the-snapgragon-ai-game/amp/">Qualcomm Ups The Snapgragon AI Game</a>
 <blockquote>
   <p>The leader in premium mobile SoCs has applied AI across the entire platform.</p>
@@ -152,7 +141,7 @@
 
 <div align="center"><h3> </h3></div>
 <a name="Samsung"></a>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Samsung_logo.png" height="35"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Samsung_logo.png" height="35"></div>
 <div align="center"><h3> </h3></div>
 <strong><a href="https://news.samsung.com/global/samsung-brings-on-device-ai-processing-for-premium-mobile-devices-with-exynos-9-series-9820-processor">Samsung Brings On-device AI Processing for Premium Mobile Devices with Exynos 9 Series 9820 Processor</a></strong>
 > Fourth-generation custom core and 2.0Gbps LTE Advanced Pro modem enables enriched mobile experiences including AR and VR applications 
@@ -162,14 +151,26 @@ Samsung resently unveiled “<a href="https://news.samsung.com/global/samsung-op
 
 <div align="center"><h3> </h3></div>
 <a name="AMD"></a>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/AMD_logo.png" height="35"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AMD_logo.png" height="35"></div>
 <div align="center"><h3> </h3></div>
-The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server-accelerators">AMD Instinct™ MI Series Accelerators</a>
-> AMD Instinct™ accelerators are engineered from the ground up for this new era of data center computing, supercharging HPC and AI workloads to propel new discoveries. The AMD Instinct™ family of accelerators can deliver industry leading performance for the data center at any scale from single server solutions up to the world’s largest supercomputers.1 With new innovations in AMD CDNA™ 2 architecture, AMD Infinity Fabric™ technology and packaging technology, the latest AMD Instinct™ accelerators are designed to power discoveries at exascale, enabling scientists to tackle our most pressing challenges.
+<p><strong><a href="https://www.amd.com/en/products/accelerators/instinct/mi350.html">AMD Instinct™ MI350 Series GPUs</a></strong></p>
+<blockquote>
+  <p>Built on 4th Gen AMD CDNA™ architecture, MI350X / MI355X deliver large HBM3E capacity (up to 288GB) and high bandwidth for generative AI training and inference, with expanded low-precision datatype support (including MXFP6 / MXFP4). MI400-series / Helios rack-scale systems are on AMD’s 2026 roadmap.</p>
+</blockquote>
+
+<p><strong><a href="https://www.amd.com/en/newsroom/press-releases/2026-2-24-amd-and-meta-announce-expanded-strategic-partnersh.html">AMD and Meta Announce Expanded Strategic Partnership to Deploy 6 Gigawatts of AMD GPUs</a></strong></p>
+<blockquote>
+  <p>Feb 2026 — AMD and Meta expanded their partnership for multi-generation Instinct GPU deployments, including custom MI450-class GPUs on the Helios rack architecture with ROCm software.</p>
+</blockquote>
+
+<p><a href="https://www.amd.com/en/products/accelerators/instinct.html">AMD Instinct™ MI Series Accelerators</a></p>
+<blockquote>
+  <p>AMD Instinct accelerators target data-center HPC and AI, combining CDNA architecture, Infinity Fabric interconnect, and the open ROCm software stack for training and inference at scale.</p>
+</blockquote>
 
 <p><a name="IBM"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/IBM_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/IBM_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -200,7 +201,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Marvell"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Marvell_logo.png" height="60"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Marvell_logo.png" height="60"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -219,10 +220,22 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Google"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Google_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Google_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus">Introducing Trillium, sixth-generation TPUs</a></strong></p>
+<blockquote>
+  <p>Google Cloud’s TPU v6e (Trillium) delivers up to 4.7× peak compute per chip vs. TPU v5e, with doubled HBM capacity/bandwidth and improved energy efficiency for foundation-model training and serving.</p>
+</blockquote>
+
+<p><strong><a href="https://docs.cloud.google.com/tpu/docs/release-notes">Cloud TPU Ironwood (TPU7x) generally available</a></strong></p>
+<blockquote>
+  <p>Ironwood is Google Cloud’s seventh-generation TPU family for large-scale training and inference (LLMs, MoEs, diffusion). See Cloud TPU docs / release notes for TPU7x availability and configurations.</p>
+</blockquote>
+
+<p><a href="https://docs.cloud.google.com/tpu/docs/v6e">TPU v6e (Trillium) architecture documentation</a></p>
+
 <p><strong><a href="https://www.zdnet.com/article/google-tensor-everything-you-need-to-know-about-the-pixel-6-chip/">Google Tensor: Everything you need to know about the Pixel 6 chip</a></strong></p>
 <blockquote>
   <p>Google has taken the wraps off its latest Pixel smartphones and, among the changes, the one with the biggest long-term impact is the switch to in-house silicon for the search giant.</p>
@@ -254,10 +267,20 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Amazon_AWS"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Amazon_AWS.png" height="50"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Amazon_AWS.png" height="50"></div>
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/">Amazon EC2 Trn2 Instances and Trn2 UltraServers</a></strong></p>
+<blockquote>
+  <p>Powered by AWS Trainium2, Trn2 instances target generative AI training and inference with large HBM capacity and NeuronLink chip-to-chip interconnect. AWS positions Trn2 for strong price/performance vs. GPU instances; UltraServers scale to dozens of Trainium2 chips.</p>
+</blockquote>
+
+<p><a href="https://aws.amazon.com/ec2/instance-types/inf2/">Amazon EC2 Inf2 (Inferentia2)</a></p>
+<blockquote>
+  <p>Inferentia2-powered Inf2 instances improve throughput and latency for large-model inference vs. first-generation Inferentia, with multi-chip scale-out via NeuronLink.</p>
+</blockquote>
+
 <p><strong><a href="https://aws.amazon.com/cn/machine-learning/trainium/">AWS Trainium</a></strong></p>
 <blockquote>
   <p>AWS Trainium is the second custom machine learning (ML) chip designed by AWS that provides the best price performance for training deep learning models in the cloud.  Trainium offers the highest performance with the most teraflops (TFLOPS) of compute power for the fastest ML training in Amazon EC2 and enables a broader set of ML applications. The Trainium chip is specifically optimized for deep learning training workloads for applications including image classification, semantic search, translation, voice recognition, natural language processing and recommendation engines.</p>
@@ -272,15 +295,20 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Microsoft"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Microsoft_logo.png" height="60"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Microsoft_logo.png" height="60"></div>
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://azure.microsoft.com/en-us/blog/azure-maia-for-the-era-of-ai-from-silicon-to-software-to-systems/">Azure Maia for the era of AI: From silicon to software to systems</a></strong></p>
+<blockquote>
+  <p>Microsoft’s first in-house AI accelerator, Azure Maia 100 (5nm, advanced packaging), is co-designed for Azure AI / Copilot-class cloud workloads, with a software stack integrating PyTorch and ONNX Runtime.</p>
+</blockquote>
+
 <div align="center"><h3> </h3></div>
 
 <p><a name="Apple"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Apple_logo.png" height="60"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Apple_logo.png" height="60"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -288,7 +316,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Alibaba"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/alibaba_logo.png" height="60"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/alibaba_logo.png" height="60"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -302,7 +330,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Tencent_Cloud"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Tencent_Cloud_logo.png" height="30"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tencent_Cloud_logo.png" height="30"></div>
 
 <div align="center"><h3> </h3></div>
 <p><a href="https://www.datacenterdynamics.com/en/news/tencent-reveals-three-data-center-chips-for-ai-video-transcoding-and-networking/">Tencent reveals three data center chips - for AI, video transcoding, and networking</a></p>
@@ -315,7 +343,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 <p><br />
 <a name="Baidu"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Baidu_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Baidu_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -328,7 +356,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Fujitsu"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Fujitsu_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Fujitsu_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -341,7 +369,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Nokia"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Nokia_logo.png" height="30"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nokia_logo.png" height="30"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -353,20 +381,30 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Facebook"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/facebook_logo.png" height="50"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/facebook_logo.png" height="50"></div>
 
 <div align="center"><h3> </h3></div>
 
-<p><a href="https://www.reuters.com/technology/facebook-developing-machine-learning-chip-information-2021-09-09/">Facebook developing machine learning chip - The Information</a></p>
+<p><strong><a href="https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/">Our next generation Meta Training and Inference Accelerator (MTIA)</a></strong></p>
+<blockquote>
+  <p>Meta’s custom MTIA family targets ranking/recommendation and broader AI workloads. Newer generations expand coverage toward GenAI inference and training, complementing GPU capacity in Meta’s AI infrastructure.</p>
+</blockquote>
+
+<p><strong><a href="https://ai.meta.com/blog/meta-mtia-scale-ai-chips-for-billions/">Four MTIA Chips in Two Years: Scaling AI Experiences for Billions</a></strong></p>
+<blockquote>
+  <p>Meta describes a multi-generation MTIA roadmap (including MTIA 300–500 class chips) with modular chiplet designs and accelerating deployment cadence through 2026–2027.</p>
+</blockquote>
+
+<p><a href="https://www.reuters.com/technology/facebook-developing-machine-learning-chip-information-2021-09-09/">Facebook developing machine learning chip - The Information</a> (historical, 2021)</p>
 <blockquote>
-  <p>Facebook Inc (FB.O) is developing a machine learning chip to handle tasks such as content recommendation to users, The Information reported on Thursday, citing two people familiar with the project.</p>
+  <p>Early reporting on Meta/Facebook’s in-house ML chip efforts for recommendation workloads.</p>
 </blockquote>
 
 <div align="center"><h3> </h3></div>
 
 <p><a name="Tesla"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Tesla_logo.png" height="60"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tesla_logo.png" height="60"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -390,7 +428,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="ARM"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/ARM_logo.png" height="30"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ARM_logo.png" height="30"></div>
 
 <div align="center"><h3> </h3></div>
 <a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-n78">NPU ETHOS-N78</a></p>
@@ -407,7 +445,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Synopsys"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Synopsys_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Synopsys_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -421,7 +459,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Imagination"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Imagination_logo.png" height="60"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Imagination_logo.png" height="60"></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -434,7 +472,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="CEVA"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/CEVA_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/CEVA_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 <p><a href="https://www.ceva-dsp.com/app/deep-learning/">Deep learning for the real-time embedded world</a></p>
@@ -445,14 +483,14 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <div align="center"><h3> </h3></div>
 <p><a name="Cadence"></a></p>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Cadence_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cadence_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 <p><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Tensilica AI Platform</a></p>
 
 <div align="center"><h3> </h3></div>
 <p><a name="VeriSilicon"></a></p>
-<div align="center"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/VeriSilicon_logo.png" height="40"></div>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/VeriSilicon_logo.png" height="40"></div>
 
 <div align="center"><h3> </h3></div>
 <p><a href="https://www.verisilicon.com/en/IPPortfolio/VivanteNPUIP">Vivante® NPU IP</a></p>
@@ -470,10 +508,15 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Cerebras"></a></p>
 
-<div align="center"><a href="https://www.cerebras.net/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Cerebras_logo.png" height="50"></a></div>
+<div align="center"><a href="https://www.cerebras.net/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cerebras_logo.png" height="50"></a></div>
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://www.cerebras.net/press-release/cerebras-announces-third-generation-wafer-scale-engine">Cerebras Systems Unveils World’s Fastest AI Chip with 4 Trillion Transistors (WSE-3)</a></strong></p>
+<blockquote>
+  <p>March 2024 — WSE-3 (5nm) powers the CS-3 system with ~900,000 AI-optimized cores and 125 petaflops peak AI performance, doubling WSE-2 performance at the same power/price point for large-model training and high-throughput inference.</p>
+</blockquote>
+
 <p><strong><a href="https://www.cerebras.net/press-release/cerebras-unveils-andromeda-a-13.5-million-core-ai-supercomputer-that-delivers-near-perfect-linear-scaling-for-large-language-models">Cerebras Unveils Andromeda, a 13.5 Million Core AI Supercomputer that Delivers Near-Perfect Linear Scaling for Large Language Models</a></strong></p>
 <blockquote>
   <p>Delivering more than 1 Exaflop of AI compute and 120 Petaflops of dense compute, Andromeda is one of the largest AI supercomputers ever built, and is dead simple to use</p>
@@ -503,7 +546,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Graphcore"></a></p>
 
-<div align="center"><a href="https://www.graphcore.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Graphcore_logo.png" height="70"></a></div>
+<div align="center"><a href="https://www.graphcore.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Graphcore_logo.png" height="70"></a></div>
 
 <p><strong><a href="https://www.eetimes.com/graphcore-supercharges-ipu-with-wafer-on-wafer/">Graphcore Supercharges IPU with Wafer-on-Wafer</a></strong></p>
 <blockquote>
@@ -528,10 +571,15 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Tenstorrent"></a></p>
 
-<div align="center"><a href="http://tenstorrent.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Tenstorrent_logo.png" height="100"></a></div>
+<div align="center"><a href="http://tenstorrent.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tenstorrent_logo.png" height="100"></a></div>
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://tenstorrent.com/">Tenstorrent Blackhole / Wormhole AI processors</a></strong></p>
+<blockquote>
+  <p>Tenstorrent builds RISC-V based AI computers (Wormhole, Blackhole) with an open software stack, targeting datacenter and edge deployments under Jim Keller’s architecture leadership.</p>
+</blockquote>
+
 <p><a href="https://www.prnewswire.com/news-releases/tenstorrent-raises-over-200-million-at-1-billion-valuation-to-create-programmable-high-performance-ai-computers-301295913.html">Tenstorrent Raises over $200 million at $1 billion Valuation to Create Programmable, High Performance AI Computers</a></p>
 <blockquote>
   <p>TORONTO, May 20, 2021 /PRNewswire/ - Tenstorrent, a hardware start-up developing next generation computers, announced today that it has raised over $200 million in a recent funding round that values the company at $1 billion. The round was led by Fidelity Management and Research Company and includes additional investments from Eclipse Ventures, Epic CG and Moore Capital. </p>
@@ -543,7 +591,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Blaize"></a></p>
 
-<div align="center"><a href="https://www.blaize.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Blaize_logo.png" height="40"></a></div>
+<div align="center"><a href="https://www.blaize.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blaize_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -556,7 +604,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Koniku"></a></p>
 
-<div align="center"><a href="http://koniku.io/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Koniku_logo.png" height="50"></a></div>
+<div align="center"><a href="http://koniku.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Koniku_logo.png" height="50"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -568,7 +616,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Adapteva"></a></p>
 
-<div align="center"><a href="http://www.adapteva.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Adapteva_logo.png" height="70"></a></div>
+<div align="center"><a href="http://www.adapteva.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Adapteva_logo.png" height="70"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -576,7 +624,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Mythic"></a></p>
 
-<div align="center"><a href="https://mythic.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Mythic_logo.png" height="20"></a></div>
+<div align="center"><a href="https://mythic.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Mythic_logo.png" height="20"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -594,7 +642,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Brainchip"></a></p>
 
-<div align="center"><a href="http://www.brainchipinc.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Brainchip_logo.png" height="40"></a></div>
+<div align="center"><a href="http://www.brainchipinc.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Brainchip_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -607,7 +655,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Deepvision"></a></p>
 
-<div align="center"><a href="https://deepvision.io/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Deepvision_logo.png" height="40"></a></div>
+<div align="center"><a href="https://deepvision.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Deepvision_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -624,6 +672,11 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Vera-Rubin-Opens-Agentic-AI-Frontier/default.aspx">NVIDIA Vera Rubin Opens Agentic AI Frontier (includes Groq LPU)</a></strong></p>
+<blockquote>
+  <p>In late 2025 NVIDIA licensed Groq’s LPU technology and brought on key engineering talent. At GTC 2026, NVIDIA described integrating Groq-class LPU engines into the Vera Rubin AI factory stack for low-latency / high-efficiency token generation alongside Rubin GPUs.</p>
+</blockquote>
+
 <p><strong><a href="https://www.eetimes.com/groq-demos-fast-llms-on-4-year-old-silicon/">Groq Demonstrates Fast LLMs on 4-Year-Old Silicon</a></strong></p>
 <blockquote>
   <p>MOUNTAIN VIEW, CALIF. — Groq has repositioned its first-generation AI inference chip as a language processing unit (LPU), and demonstrated Meta’s Llama-2 70-billion–parameter large language model (LLM) running inference at 240 tokens per second per user. Groq CEO Jonathan Ross told EE Times that the company had Llama-2 up and running on the company’s 10-rack (64-chip) cloud-based dev system in “a couple of days.” This system is based on the company’s first gen AI silicon, released four years ago.</p>
@@ -638,7 +691,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Kneron"></a></p>
 
-<div align="center"><a href="http://www.kneron.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Kneron_logo.png" height="60"></a></div>
+<div align="center"><a href="http://www.kneron.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Kneron_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -648,7 +701,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="GTI"></a></p>
 
-<div align="center"><a href="https://www.gyrfalcontech.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/GTI_Logo.png" height="40"></a></div>
+<div align="center"><a href="https://www.gyrfalcontech.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GTI_Logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -662,7 +715,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="SambaNova"></a></p>
 
-<div align="center"><a href="https://sambanovasystems.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/SambaNova_logo.png" height="40"></a></div>
+<div align="center"><a href="https://sambanovasystems.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SambaNova_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -689,7 +742,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <div align="center"><h3> </h3></div>
 <p><a name="GreenWaves"></a></p>
-<div align="center"><a href="https://greenwaves-technologies.com/en/greenwaves-technologies-2/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/GreenWaves_logo.png" height="50"></a></div>
+<div align="center"><a href="https://greenwaves-technologies.com/en/greenwaves-technologies-2/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GreenWaves_logo.png" height="50"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><a href="https://www.eetimes.eu/greenwaves-shows-off-advanced-audio-demos/">GreenWaves Shows Off Advanced Audio Demos</a></p>
@@ -701,7 +754,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Lightelligence"></a></p>
 
-<div align="center"><a href="https://www.lightelligence.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Lightelligence_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.lightelligence.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightelligence_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -714,7 +767,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Lightmatter"></a></p>
 
-<div align="center"><a href="https://www.lightmatter.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Lightmatter_logo.png" height="50"></a></div>
+<div align="center"><a href="https://www.lightmatter.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightmatter_logo.png" height="50"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -725,7 +778,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Hailo"></a></p>
 
-<div align="center"><a href="https://www.hailotech.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Hailo_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.hailotech.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Hailo_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -738,7 +791,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Tachyum"></a></p>
 
-<div align="center"><a href="http://www.tachyum.com"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Tachyum_logo.png" height="40"></a></div>
+<div align="center"><a href="http://www.tachyum.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tachyum_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -751,7 +804,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Alphaics"></a></p>
 
-<div align="center"><a href="https://www.alphaics.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Alphaics_logo.png" height="50"></a></div>
+<div align="center"><a href="https://www.alphaics.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Alphaics_logo.png" height="50"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -764,7 +817,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Syntiant"></a></p>
 
-<div align="center"><a href="https://www.syntiant.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Syntiant_logo.png" height="30"></a></div>
+<div align="center"><a href="https://www.syntiant.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Syntiant_logo.png" height="30"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -778,7 +831,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="aiCTX"></a></p>
 
-<div align="center"><a href="https://aictx.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/aiCTX_logo.png" height="40"></a></div>
+<div align="center"><a href="https://aictx.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/aiCTX_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -790,7 +843,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Flexlogix"></a></p>
 
-<div align="center"><a href="http://www.flex-logix.com/nmax"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/flexlogix_logo.png" height="40"></a></div>
+<div align="center"><a href="http://www.flex-logix.com/nmax"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/flexlogix_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -802,7 +855,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="PFN"></a></p>
 
-<div align="center"><a href="https://projects.preferred.jp/mn-core/en/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/PFN_logo.png" height="40"></a></div>
+<div align="center"><a href="https://projects.preferred.jp/mn-core/en/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/PFN_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -815,7 +868,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Cornami"></a></p>
 
-<div align="center"><a href="http://cornami.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Cornami_logo.jpg" height="30"></a></div>
+<div align="center"><a href="http://cornami.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cornami_logo.jpg" height="30"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -828,7 +881,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Anaflash"></a></p>
 
-<div align="center"><a href="http://anaflash.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Anaflash_logo.png" height="40"></a></div>
+<div align="center"><a href="http://anaflash.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Anaflash_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -841,7 +894,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Optalysys"></a></p>
 
-<div align="center"><a href="https://www.optalysys.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Optalysys_logo.png" height="40"></a></div>
+<div align="center"><a href="https://www.optalysys.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Optalysys_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -854,7 +907,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="etacompute"></a></p>
 
-<div align="center"><a href="https://etacompute.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/etacompute_logo.png" height="80"></a></div>
+<div align="center"><a href="https://etacompute.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/etacompute_logo.png" height="80"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -870,7 +923,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Achronix"></a></p>
 
-<div align="center"><a href="https://www.achronix.com/product/speedster7t/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Achronix_logo.png" height="30"></a></div>
+<div align="center"><a href="https://www.achronix.com/product/speedster7t/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Achronix_logo.png" height="30"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -882,7 +935,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Areanna"></a></p>
 
-<div align="center"><a href="https://areanna-ai.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Areanna_logo.png" height="60"></a></div>
+<div align="center"><a href="https://areanna-ai.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Areanna_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -894,7 +947,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Neuroblade"></a></p>
 
-<div align="center"><a href="https://www.neuroblade.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Neuroblade_logo.png" height="120"></a></div>
+<div align="center"><a href="https://www.neuroblade.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neuroblade_logo.png" height="120"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -906,7 +959,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Luminous"></a></p>
 
-<div align="center"><a href="https://www.luminouscomputing.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Luminous_logo.png" height="90"></a></div>
+<div align="center"><a href="https://www.luminouscomputing.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Luminous_logo.png" height="90"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -918,7 +971,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Efinix"></a></p>
 
-<div align="center"><a href="https://www.efinixinc.com"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Efinix_logo.png" height="25"></a></div>
+<div align="center"><a href="https://www.efinixinc.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Efinix_logo.png" height="25"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -930,7 +983,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="AIstorm"></a></p>
 
-<div align="center"><a href="https://aistorm.ai"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/AIstorm_logo.png" height="60"></a></div>
+<div align="center"><a href="https://aistorm.ai"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AIstorm_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -943,7 +996,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="SiMa"></a></p>
 
-<div align="center"><a href="http://sima.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/SiMa_logo.png" height="40"></a></div>
+<div align="center"><a href="http://sima.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SiMa_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -959,7 +1012,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Untether"></a></p>
 
-<div align="center"><a href="https://untether.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Untether_logo.png" height="40"></a></div>
+<div align="center"><a href="https://untether.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Untether_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -971,7 +1024,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="GrAI"></a></p>
 
-<div align="center"><a href="https://www.graimatterlabs.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/GrAI_logo.png" height="40"></a></div>
+<div align="center"><a href="https://www.graimatterlabs.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GrAI_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -983,7 +1036,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Rain"></a></p>
 
-<div align="center"><a href="http://rain-neuromorphics.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Rain_logo.png" height="40"></a></div>
+<div align="center"><a href="http://rain-neuromorphics.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Rain_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -994,7 +1047,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="ABR"></a></p>
 
-<div align="center"><a href="https://appliedbrainresearch.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/ABR_logo.png" height="40"></a></div>
+<div align="center"><a href="https://appliedbrainresearch.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ABR_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1005,7 +1058,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Xmos"></a></p>
 
-<div align="center"><a href="https://www.xmos.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Xmos_logo.png" height="40"></a></div>
+<div align="center"><a href="https://www.xmos.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Xmos_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1021,7 +1074,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="DinoplusAI"></a></p>
 
-<div align="center"><a href="http://dinoplus.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/DinoplusAI_logo.png" height="60"></a></div>
+<div align="center"><a href="http://dinoplus.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/DinoplusAI_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1031,7 +1084,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Furiosa"></a></p>
 
-<div align="center"><a href="https://www.furiosa.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Furiosa_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.furiosa.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Furiosa_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1041,7 +1094,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Corerain"></a></p>
 
-<div align="center"><a href="http://www.corerain.com/en"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Corerain_logo.png" height="60"></a></div>
+<div align="center"><a href="http://www.corerain.com/en"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Corerain_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1051,7 +1104,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="Perceive"></a></p>
 
-<div align="center"><a href="https://perceive.io/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Perceive_logo.png" height="60"></a></div>
+<div align="center"><a href="https://perceive.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Perceive_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1062,7 +1115,7 @@ The soon to be released <a href="https://www.amd.com/en/graphics/instinct-server
 
 <p><a name="SimpleMachines"></a></p>
 
-<div align="center"><a href="https://www.simplemachines.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/SimpleMachines_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.simplemachines.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SimpleMachines_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1075,7 +1128,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <p><a name="Neureality"></a></p>
 
-<div align="center"><a href="https://www.neureality.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Neureality_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.neureality.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neureality_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1095,7 +1148,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote>
  
 <p><a name="AnalogInference"></a></p>
-<div align="center"><a href="https://www.analog-inference.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/AnalogInference_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.analog-inference.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AnalogInference_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.eenewsanalog.com/news/analog-inference-startup-raises-106-million">Analog inference startup raises $10.6 million</a></strong></p>
@@ -1104,7 +1157,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote>
 
 <p><a name="Quadric"></a></p>
-<div align="center"><a href="https://www.quadric.io/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Quatric_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.quadric.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Quatric_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.hpcwire.com/off-the-wire/quadric-announces-unified-silicon-and-software-platform-optimized-for-on-device-ai/">Quadric Announces Unified Silicon and Software Platform Optimized for On-Device AI</a></strong></p>
@@ -1113,7 +1166,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote> 
 
 <p><a name="EdgeQ"></a></p>
-<div align="center"><a href="https://edgeq.io/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/EdgeQ_logo.png" height="60"></a></div>
+<div align="center"><a href="https://edgeq.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/EdgeQ_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><strong><a href="https://techcrunch.com/2021/01/26/edgeq-reveals-more-details-behind-its-next-gen-5g-ai-chip/">EdgeQ reveals more details behind its next-gen 5G/AI chip</a></strong></p>
@@ -1122,7 +1175,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote> 
  
 <p><a name="Innatera"></a></p>
-<div align="center"><a href="http://www.innatera.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Innatera_logo.png" height="60"></a></div>
+<div align="center"><a href="http://www.innatera.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Innatera_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.eetimes.com/innatera-unveils-neuromorphic-ai-chip-to-accelerate-spiking-networks/">Innatera Unveils Neuromorphic AI Chip to Accelerate Spiking Networks</a></strong></p>
@@ -1131,7 +1184,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote> 
  
 <p><a name="Ceremorphic"></a></p>
-<div align="center"><a href="https://ceremorphic.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Ceremorphic_logo.png" height="60"></a></div>
+<div align="center"><a href="https://ceremorphic.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Ceremorphic_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.eetimes.com/redpine-founder-launches-ai-processor-startup/">Redpine Founder Launches AI Processor Startup</a></strong></p>
@@ -1140,7 +1193,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote> 
  
 <p><a name="Aspinity"></a></p>
-<div align="center"><a href="https://www.aspinity.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Aspinity_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.aspinity.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Aspinity_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><strong><a href="https://embeddedcomputing.com/technology/analog-and-power/analog-semicundoctors-sensors/aspinity-analog-ml-chip-allows-battery-powered-always-on">Aspinity Analog ML Chip Allows Battery-Powered “Always On”</a></strong></p>
@@ -1149,7 +1202,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote> 
  
 <p><a name="Teramem"></a></p>
-<div align="center"><a href="https://www.tetramem.com/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/Teramem_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.tetramem.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Teramem_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.tetramem.com/posts/TetraMem-Technology-Debut-at-Linley">TetraMem enjoyed an exciting public debut of our analog in-memory compute technology at the Linley Spring 2022 Processor Conference.</a></strong></p>
@@ -1158,7 +1211,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote> 
  
 <p><a name="d-matrix"></a></p>
-<div align="center"><a href="https://www.d-matrix.ai/"><img src="https://github.com/basicmi/Deep-Learning-Processor-List/raw/master/resource/d-matrix_logo.png" height="60"></a></div>
+<div align="center"><a href="https://www.d-matrix.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/d-matrix_logo.png" height="60"></a></div>
 
 <div align="center"><h3> </h3></div>
 
@@ -1179,20 +1232,24 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 <div align="center"><h2>AI Chip Compilers</h2></div>
 
 <p><HR>
-1. <a href="https://github.com/pytorch/glow">pytorch/glow</a><br>
-2. <a href="https://tvm.ai/">TVM:End to End Deep Learning Compiler Stack</a><br>
-3. <a href="https://www.tensorflow.org/xla">Google Tensorflow XLA</a><br>
-4. <a href="https://developer.nvidia.com/tensorrt">Nvidia TensorRT</a><br>
-5. <a href="https://github.com/plaidml/plaidml">PlaidML</a><br>
-6. <a href="https://github.com/NervanaSystems/ngraph">nGraph</a><br>
-7. <a href="https://github.com/Tiramisu-Compiler/tiramisu">MIT Tiramisu compiler</a><br>
-8. <a href="https://onnc.ai/">ONNC (Open Neural Network Compiler)</a><br>
-9. <a href="https://mlir.llvm.org/">MLIR: Multi-Level Intermediate Representation</a><br>
-10. <a href="http://tensor-compiler.org/">The Tensor Algebra Compiler (taco)</a><br>
-11. <a href="https://facebookresearch.github.io/TensorComprehensions/">Tensor Comprehensions</a><br>
-12. <a href="https://www.polymagelabs.com//">PolyMage Labs</a><br>
-13. <a href="https://octoml.ai/">OctoML</a><br>
-14. <a href="https://www.modular.com/">Modular AI</a><br>
+<strong>Actively used / widely referenced (2024–2026)</strong><br>
+1. <a href="https://mlir.llvm.org/">MLIR</a> — Multi-Level Intermediate Representation (LLVM)<br>
+2. <a href="https://tvm.apache.org/">Apache TVM</a> — end-to-end ML compiler stack<br>
+3. <a href="https://www.tensorflow.org/xla">XLA</a> — TensorFlow / JAX compiler<br>
+4. <a href="https://github.com/triton-lang/triton">OpenAI Triton</a> — GPU kernel DSL widely used for LLM kernels<br>
+5. <a href="https://iree.dev/">IREE</a> — MLIR-based end-to-end compiler &amp; runtime<br>
+6. <a href="https://developer.nvidia.com/tensorrt">NVIDIA TensorRT</a> / <a href="https://github.com/NVIDIA/TensorRT-LLM">TensorRT-LLM</a><br>
+7. <a href="https://awsdocs-neuron.readthedocs-hosted.com/">AWS Neuron</a> — Trainium / Inferentia compiler stack<br>
+8. <a href="https://rocm.docs.amd.com/">AMD ROCm</a> / composable kernel ecosystem<br>
+9. <a href="https://www.modular.com/max/mojo">Modular MAX / Mojo</a><br>
+10. <a href="https://onnxruntime.ai/">ONNX Runtime</a> — cross-vendor inference<br>
+<br>
+<strong>Historical / research compilers</strong><br>
+11. <a href="https://github.com/pytorch/glow">PyTorch Glow</a><br>
+12. <a href="https://github.com/plaidml/plaidml">PlaidML</a><br>
+13. <a href="https://github.com/Tiramisu-Compiler/tiramisu">MIT Tiramisu</a><br>
+14. <a href="http://tensor-compiler.org/">TACO (Tensor Algebra Compiler)</a><br>
+15. <a href="https://github.com/NervanaSystems/ngraph">Intel nGraph</a> (archived)<br>
 
 <div align="center"><h3> </h3></div>
 
@@ -1202,17 +1259,16 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <p><HR>
 
-1. <a href="https://dawn.cs.stanford.edu/benchmark/index.html">DAWNBench:An End-to-End Deep Learning Benchmark and Competition Image Classification (ImageNet)</a><br>
-2. <a href="https://github.com/rdadolf/fathom">Fathom:Reference workloads for modern deep learning methods</a><br>
-3. <a href="https://mlperf.org/">MLPerf:A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms</a>. 
-<strong>You can find latest MLPerf results: training 2.1, HPC 2.0, inference tiny 1.0 <a href="https://mlcommons.org/en/news/mlperf-training-4q2022/">here.</a></strong>. <br>
-<strong>You can find MLPerf inference results v2.1 <a href="https://mlcommons.org/en/news/mlperf-inference-v21/">here.</a></strong>. <br>
-<strong>You can find MLPerf training results v1.0 <a href="https://mlcommons.org/en/news/mlperf-training-2q2022/">here.</a></strong>. <br>
-
-4. <a href="https://aimatrix.ai/en-us/index.html">AI Matrix</a><br>
-5. <a href="http://ai-benchmark.com/index.html">AI-Benchmark</a><br>
-6. <a href="https://github.com/AIIABenchmark/AIIA-DNN-benchmark">AIIABenchmark</a><br>
-7. <a href="https://www.eembc.org/mlmark/">EEMBC MLMark Benchmark</a><br>
+1. <a href="https://mlcommons.org/benchmarks/">MLPerf (MLCommons)</a> — industry-standard training, inference, HPC, and client benchmarks.<br>
+<strong>Latest results (2026):</strong>
+<a href="https://mlcommons.org/2026/04/mlperf-inference-v6-0-results/">Inference v6.0</a> ·
+<a href="https://mlcommons.org/2026/06/mlperf-training-v6-0-results/">Training v6.0</a> ·
+<a href="https://mlcommons.org/visualizer">Results visualizer</a><br>
+2. <a href="https://dawn.cs.stanford.edu/benchmark/index.html">DAWNBench</a> (historical end-to-end ImageNet benchmark)<br>
+3. <a href="https://github.com/rdadolf/fathom">Fathom</a> — reference workloads for modern deep learning methods<br>
+4. <a href="http://ai-benchmark.com/index.html">AI-Benchmark</a> — mobile / edge AI scores<br>
+5. <a href="https://www.eembc.org/mlmark/">EEMBC MLMark</a><br>
+6. <a href="https://github.com/AIIABenchmark/AIIA-DNN-benchmark">AIIA DNN Benchmark</a><br>
 
 <div align="center"><h3> </h3></div>
 
@@ -1229,10 +1285,10 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 3. <a href="http://eyeriss.mit.edu/tutorial.html">Tutorial on Hardware Architectures for Deep Neural Networks</a><br>
 4. <strong><a href="https://nicsefc.ee.tsinghua.edu.cn/projects/neural-network-accelerator/">Neural Network Accelerator Comparison</a></strong><br>
 5. "White Paper on AI Chip Technologies 2018". You can download it from <a href="https://cloud.tsinghua.edu.cn/f/9aa0a4f0a5684cc48495/?dl=1">here</a>, or <a href="https://drive.google.com/open?id=1ieDm0bpjVWl5MnSESRs92EcmoSzG5vcm">Google drive.</a><br>
-5. <strong>"What We Talk About When We Talk About AI Chip". <a href="https://mp.weixin.qq.com/s/SbX5yz5d3GXaLcl15DO6OQ">#1</a>,  <a href="https://mp.weixin.qq.com/s/zvgDgKpIMIRLFUEW0fFOeg">#2</a>,  <a href="https://mp.weixin.qq.com/s/CKHs5yblcMur4h2BwUBICw">#3</a>,  <a href="https://mp.weixin.qq.com/s/hFnHhaWWYTFRUsD3HlMbLw">#4</a></strong><br>
-6. <strong><a href="https://birenresearch.github.io/AIChip_Paper_List/">AI Chip Paper List</a></strong><br>
-7. <strong><a href="https://khairy2011.medium.com/tpu-vs-gpu-vs-cerebras-vs-graphcore-a-fair-comparison-between-ml-hardware-3f5a19d89e38">TPU vs GPU vs Cerebras vs Graphcore: A Fair Comparison between ML Hardware</a></strong><br>
+6. <strong>"What We Talk About When We Talk About AI Chip". <a href="https://mp.weixin.qq.com/s/SbX5yz5d3GXaLcl15DO6OQ">#1</a>,  <a href="https://mp.weixin.qq.com/s/zvgDgKpIMIRLFUEW0fFOeg">#2</a>,  <a href="https://mp.weixin.qq.com/s/CKHs5yblcMur4h2BwUBICw">#3</a>,  <a href="https://mp.weixin.qq.com/s/hFnHhaWWYTFRUsD3HlMbLw">#4</a></strong><br>
+7. <strong><a href="https://birenresearch.github.io/AIChip_Paper_List/">AI Chip Paper List</a></strong><br>
+8. <strong><a href="https://khairy2011.medium.com/tpu-vs-gpu-vs-cerebras-vs-graphcore-a-fair-comparison-between-ml-hardware-3f5a19d89e38">TPU vs GPU vs Cerebras vs Graphcore: A Fair Comparison between ML Hardware</a></strong><br>
 
 <div align="center">
-<a href="http://www.reliablecounter.com" target="_blank"><img src="http://www.reliablecounter.com/count.php?page=https://basicmi.github.io/Deep-Learning-Processor-List/&digit=style/plain/3/&reloads=1" alt="laptop" title="laptop" border="0"></a>
+<a href="http://www.reliablecounter.com" target="_blank"><img src="http://www.reliablecounter.com/count.php?page=https://basicmi.github.io/AI-Chip/&digit=style/plain/3/&reloads=1" alt="laptop" title="laptop" border="0"></a>
 </div>

From 2a4a3e069cd3102d4b5e9564d650fd3274268f51 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 02:07:11 +0000
Subject: [PATCH 02/13] Expand coverage: Apple/Samsung/Qualcomm, China
 accelerators, Etched

Add on-device SoC updates (M4/M5, Exynos 2500, Snapdragon 8 Elite), Alibaba
T-Head PPU/Zhenwu, new China AI accelerator section (Huawei Ascend, Cambricon,
Biren, Moore Threads, Enflame, Horizon), and Etched transformer ASIC startup.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md | 168 ++++++++++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 156 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 25b3833..10ae4a0 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,12 @@
 <li><strong>Groq</strong>: LPU inference focus; technology licensed / team joined <a href="#Groq">NVIDIA</a> (late 2025).</li>
 <li><strong>Benchmarks</strong>: Links updated to <a href="#AIChipBenchmarks">MLPerf Inference v6.0</a> and <a href="#AIChipBenchmarks">Training v6.0</a>.</li>
 <li><strong>Compilers</strong>: Refreshed <a href="#AIChipCompilers">AI Chip Compilers</a> list (Triton, IREE, TensorRT-LLM, etc.).</li>
+<li><strong>Apple</strong>: <a href="#Apple">M4 / M5</a> Apple silicon Neural Engine &amp; GPU Neural Accelerators.</li>
+<li><strong>Samsung</strong>: <a href="#Samsung">Exynos 2500</a> on-device NPU (3nm GAA).</li>
+<li><strong>Qualcomm</strong>: <a href="#Qualcomm">Snapdragon 8 Elite</a> Hexagon NPU for on-device GenAI.</li>
+<li><strong>China accelerators</strong>: new section for <a href="#China_AI_Chips">Huawei Ascend</a>, <a href="#Cambricon">Cambricon</a>, <a href="#Biren">Biren</a>, <a href="#MooreThreads">Moore Threads</a>, etc.</li>
+<li><strong>Alibaba</strong>: T-Head <a href="#Alibaba">PPU / Zhenwu</a> cloud AI chips.</li>
+<li><strong>Startups</strong>: added <a href="#Etched">Etched</a> (transformer ASIC / Sohu).</li>
 <li><strong>Repo hygiene</strong>: Fixed asset links to this repository; cleaned header WeChat album links.</li>
 </ul>
 
@@ -44,7 +50,11 @@
   </tr>
   <tr>  
     <th><a href="#Startups">Startups</a></th>
-    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Blaize">Blaize</a>, <a href="#Koniku">Koniku</a>, <a href="#Adapteva">Adapteva</a>, <a href="#Mythic">Mythic</a>, <a href="#Brainchip">BrainChip</a>, <a href="#Leepmind">Leepmind</a>, <a href="#Groq">Groq</a>, <a href="#Kneron">Kneron</a>, <a href="#Esperanto">Esperanto Technologies</a>, <a href="#GTI">Gyrfalcon Technology</a>, <a href="#SambaNova">SambaNova Systems</a>, <a href="#GreenWaves">GreenWaves Technology</a>, <a href="#Lightelligence">Lightelligence</a>, <a href="#Lightmatter">Lightmatter</a>, <a href="#Hailo">Hailo</a>,<a href="#Tachyum">Tachyum</a>,<a href="#Alphaics">AlphaICs</a>,<a href="#Syntiant">Syntiant</a>, <a href="#aiCTX">aiCTX</a>, <a href="#Flexlogix">Flex Logix</a>, <a href="#PFN">Preferred Network</a>, <a href="#Cornami">Cornami</a>, <a href="#Anaflash">Anaflash</a>, <a href="#Optalysys">Optaylsys</a>, <a href="#etacompute">Eta Compute</a>, <a href="#Achronix">Achronix</a>, <a href="#Areanna">Areanna AI</a>, <a href="#Neuroblade">Neuroblade</a>, <a href="#Luminous">Luminous Computing</a>, <a href="#Efinix">Efinix</a>, <a href="#AIstorm">AISTORM</a>, <a href="#SiMa">SiMa.ai</a>,<a href="#Untether">Untether AI</a>, <a href="#GrAI">GrAI Matter Lab</a>, <a href="#Rain">Rain Neuromorphics</a>, <a href="#ABR">Applied Brain Research</a>, <a href="#Xmos">XMOS</a>, <a href="#DinoplusAI">DinoPlusAI</a>, <a href="#Furiosa">Furiosa AI</a>, <a href="#Perceive">Perceive</a>, <a href="#SimpleMachines">SimpleMachines</a>, <a href="#Neureality">Neureality</a>, <a href="#AnalogInference">Analog Inference</a>, <a href="#Quadric">Quadric</a>, <a href="#EdgeQ">EdgeQ</a>, <a href="#Innatera">Innatera Nanosystems</a>, <a href="#Ceremorphic">Ceremorphic</a>, <a href="#Aspinity">Aspinity</a>, <a href="#Teramem">TeraMem</a>, <a href="#d-matrix">d-Matrix</a></td>
+    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Etched">Etched</a>, <a href="#Groq">Groq</a>, <a href="#SambaNova">SambaNova</a>, <a href="#d-matrix">d-Matrix</a>, <a href="#Furiosa">Furiosa AI</a>, … (full list in section IV)</td>
+  </tr>
+  <tr>
+    <th><a href="#China_AI_Chips">China AI Accelerators</a></th>
+    <td><a href="#HuaweiAscend">Huawei Ascend</a>, <a href="#Cambricon">Cambricon</a>, <a href="#Biren">Biren</a>, <a href="#MooreThreads">Moore Threads</a>, <a href="#Enflame">Enflame</a>, <a href="#Horizon">Horizon Robotics</a>, <a href="#Alibaba">Alibaba T-Head</a>, <a href="#Baidu">Baidu Kunlun</a></td>
   </tr>
 </table>
 
@@ -124,9 +134,14 @@
 <div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Qualcomm_logo.png" height="40"></div>
 <div align="center"><h3> </h3></div>
 
-<p><strong><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Qualcomm Cloud AI 100 / Hexagon NPU</a></strong></p>
+<p><strong><a href="https://www.androidauthority.com/snapdragon-8-elite-3491805/">Snapdragon 8 Elite — Hexagon NPU for on-device GenAI</a></strong></p>
+<blockquote>
+  <p>Snapdragon Summit 2024 — custom Oryon CPU plus a redesigned Hexagon NPU (fused scalar/vector/tensor accelerators). Qualcomm cites ~45% faster NPU performance and better perf/W vs. Snapdragon 8 Gen 3 for multimodal on-device generative AI.</p>
+</blockquote>
+
+<p><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Qualcomm Cloud AI 100</a></p>
 <blockquote>
-  <p>Qualcomm continues to push on-device and edge/cloud AI via Snapdragon platforms (Hexagon NPU) and Cloud AI accelerators for efficient inference outside traditional GPU clusters.</p>
+  <p>Cloud/edge inference accelerator focused on power-efficient deployment outside traditional GPU clusters.</p>
 </blockquote>
 
 <a href="https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/karlfreund/2022/11/16/qualcomm-ups-the-snapgragon-ai-game/amp/">Qualcomm Ups The Snapgragon AI Game</a>
@@ -143,11 +158,15 @@
 <a name="Samsung"></a>
 <div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Samsung_logo.png" height="35"></div>
 <div align="center"><h3> </h3></div>
-<strong><a href="https://news.samsung.com/global/samsung-brings-on-device-ai-processing-for-premium-mobile-devices-with-exynos-9-series-9820-processor">Samsung Brings On-device AI Processing for Premium Mobile Devices with Exynos 9 Series 9820 Processor</a></strong>
-> Fourth-generation custom core and 2.0Gbps LTE Advanced Pro modem enables enriched mobile experiences including AR and VR applications 
+<p><strong><a href="https://semiconductor.samsung.com/processor/mobile-processor/exynos-2500/">Samsung Exynos 2500</a></strong></p>
+<blockquote>
+  <p>Flagship mobile SoC on Samsung 3nm GAA process with a substantially upgraded NPU (Samsung cites up to ~59 TOPS and ~39% better on-device AI vs. Exynos 2400) for generative AI and privacy-preserving on-device inference.</p>
+</blockquote>
 
-<br> 
-Samsung resently unveiled “<a href="https://news.samsung.com/global/samsung-optimizes-premium-exynos-9-series-9810-for-ai-applications-and-richer-multimedia-content">The new Exynos 9810 brings premium features with a 2.9GHz custom CPU, an industry-first 6CA LTE modem and deep learning processing capabilities</a>”.   
+<p><a href="https://news.samsung.com/global/samsung-brings-on-device-ai-processing-for-premium-mobile-devices-with-exynos-9-series-9820-processor">Samsung Brings On-device AI Processing… Exynos 9 Series 9820</a> (historical)</p>
+<blockquote>
+  <p>Earlier on-device AI messaging around Exynos 9820 / 9810-era NPU features for AR/VR and mobile deep learning.</p>
+</blockquote>   
 
 <div align="center"><h3> </h3></div>
 <a name="AMD"></a>
@@ -312,6 +331,21 @@ Samsung resently unveiled “<a href="https://news.samsung.com/global/samsung-op
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/">Apple unleashes M5, the next big leap in AI performance for Apple silicon</a></strong></p>
+<blockquote>
+  <p>Oct 2025 — M5 (3nm) adds Neural Accelerators in each GPU core and a faster 16-core Neural Engine, targeting on-device Apple Intelligence and local AI workloads across MacBook Pro, iPad Pro, and Vision Pro.</p>
+</blockquote>
+
+<p><strong><a href="https://www.apple.com/newsroom/2026/03/apple-debuts-m5-pro-and-m5-max-to-supercharge-the-most-demanding-pro-workflows/">Apple debuts M5 Pro and M5 Max</a></strong></p>
+<blockquote>
+  <p>Mar 2026 — Fusion Architecture scales GPU AI compute and unified memory bandwidth (up to 128GB / ~614GB/s on M5 Max) for local LLM prompt processing and pro creative AI workflows.</p>
+</blockquote>
+
+<p><a href="https://www.apple.com/newsroom/2024/05/apple-introduces-m4-chip/">Apple introduces M4 chip</a></p>
+<blockquote>
+  <p>May 2024 — M4 Neural Engine up to ~38 TOPS, with CPU ML accelerators and high-bandwidth unified memory for on-device AI on iPad Pro and later Macs.</p>
+</blockquote>
+
 <div align="center"><h3> </h3></div>
 
 <p><a name="Alibaba"></a></p>
@@ -320,10 +354,19 @@ Samsung resently unveiled “<a href="https://news.samsung.com/global/samsung-op
 
 <div align="center"><h3> </h3></div>
 
-<p><a href="https://medium.com/syncedreview/alibabas-new-ai-chip-can-process-nearly-80k-images-per-second-63412dec22a3">Alibaba’s New AI Chip Can Process Nearly 80K Images Per Second</a></p>
+<p><strong><a href="https://www.eetimes.com/alibaba-unveils-own-ai-chip-mounting-direct-challenge-to-nvidia/">Alibaba Unveils Own AI Chip, Mounting Direct Challenge to Nvidia</a></strong></p>
+<blockquote>
+  <p>Sep 2025 — Alibaba’s T-Head Parallel Processing Unit (PPU / Zhenwu family) targets cloud AI training and inference as a domestic alternative under export-control constraints. Reports cite ~96GB HBM2e-class memory and high chip-to-chip bandwidth; deployed in large China Unicom / Alibaba Cloud clusters.</p>
+</blockquote>
+
+<p><a href="https://www.alibabacloud.com/blog/alibaba-unveils-new-ai-chip-flagship-model-and-rebuilt-cloud-stack-ai-for-agentic-era_603151">Alibaba Cloud Summit: Zhenwu M890 and agentic AI stack</a></p>
+<blockquote>
+  <p>T-Head’s newer Zhenwu-class accelerators (e.g. M890 messaging) emphasize larger on-package memory, low-precision formats (down to FP4), and cluster interconnect / SAIL software for agentic cloud workloads.</p>
+</blockquote>
 
+<p><a href="https://medium.com/syncedreview/alibabas-new-ai-chip-can-process-nearly-80k-images-per-second-63412dec22a3">Alibaba’s Hanguang 800</a> (historical, 2019)</p>
 <blockquote>
-  <p>At the Alibaba Cloud (Aliyun) Apsara Conference 2019, Pingtouge unveiled its first AI dedicated processor for cloud-based large-scale AI inferencing. The Hanguang 800 is the first semiconductor product in Alibaba’s 20-year history.</p>
+  <p>Pingtouge / T-Head’s first AI inference ASIC, highlighted for high images-per-second inference throughput at Apsara Conference 2019.</p>
 </blockquote>
 
 <div align="center"><h3> </h3></div>
@@ -347,9 +390,14 @@ Samsung resently unveiled “<a href="https://news.samsung.com/global/samsung-op
 
 <div align="center"><h3> </h3></div>
 
-<p><a href="https://www.reuters.com/technology/baidu-says-2nd-gen-kunlun-ai-chips-enter-mass-production-2021-08-18/">Baidu says 2nd-gen Kunlun AI chips enter mass production</a></p>
+<p><strong><a href="https://www.reuters.com/technology/baidu-says-2nd-gen-kunlun-ai-chips-enter-mass-production-2021-08-18/">Baidu Kunlun / Kunlunxin AI accelerators</a></strong></p>
+<blockquote>
+  <p>Baidu’s Kunlunxin line (Kunlun chips) continues as a domestic cloud/training &amp; inference option alongside hyperscaler custom silicon. 2nd-gen Kunlun entered mass production in 2021; later generations target large-model clusters inside China.</p>
+</blockquote>
+
+<p><a href="https://www.reuters.com/technology/baidu-says-2nd-gen-kunlun-ai-chips-enter-mass-production-2021-08-18/">Baidu says 2nd-gen Kunlun AI chips enter mass production</a> (2021)</p>
 <blockquote>
-  <p>Chinese tech giant Baidu said on Wednesday it had begun mass-producing second-generation Kunlun artificial intelligence (AI) chips, as it races to become a key player in the chip industry which Beijing is trying to strengthen.</p>
+  <p>Early mass-production milestone for Baidu’s second-generation Kunlun AI chips.</p>
 </blockquote>
 
 <div align="center"><h3> </h3></div>
@@ -1215,7 +1263,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <div align="center"><h3> </h3></div>
 
-<p><strong><a href="www.reuters.com/technology/ai-chip-startup-d-matrix-raises-110-mln-with-backing-microsoft-2023-09-06/">Exclusive: AI chip startup d-Matrix raises $110 million with backing from Microsoft</a></strong></p>
+<p><strong><a href="https://www.reuters.com/technology/ai-chip-startup-d-matrix-raises-110-mln-with-backing-microsoft-2023-09-06/">Exclusive: AI chip startup d-Matrix raises $110 million with backing from Microsoft</a></strong></p>
 <blockquote>
   <p>Sept 6 (Reuters) - Silicon Valley-based artificial intelligence chip startup d-Matrix has raised $110 million from investors that include Microsoft Corp (MSFT.O) at a time when many chip companies are struggling to raise cash.</p>
 </blockquote> 
@@ -1227,6 +1275,102 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
  
 <div align="center"><h3> </h3></div>
 
+
+<p><a name="Etched"></a></p>
+<div align="center"><h3><a href="https://www.etched.com/">Etched</a></h3></div>
+
+<p><strong><a href="https://techcrunch.com/2026/06/30/nvidia-competitor-etched-hits-5b-valuation-1b-in-sales-for-ai-chip/">Nvidia competitor Etched hits $5B valuation, $1B in sales for AI chip</a></strong></p>
+<blockquote>
+  <p>Jun 2026 — Etched reports TSMC-manufactured transformer-specialized ASICs powering “frontier inference clusters,” with ~$800M raised to date (including a $500M round at a $5B valuation) and ~$1B in booked system orders.</p>
+</blockquote>
+
+<p><a href="https://techcrunch.com/2024/06/25/etched-is-building-an-ai-chip-that-only-runs-transformer-models/">Etched is building an AI chip that only runs one type of model</a></p>
+<blockquote>
+  <p>2024 Series A coverage of Sohu: an ASIC purpose-built for Transformer inference (rather than general-purpose GPU programmability), aiming for higher tokens/sec and better energy efficiency on LLM serving.</p>
+</blockquote>
+
+<div align="center"><h3> </h3></div>
+
+
+<div align="center"><h2><a name="China_AI_Chips"></a>V. China AI Accelerator Vendors</h2></div>
+<p><HR></p>
+<p>Domestic training/inference accelerators have become central under export-control and procurement policies. Coverage below highlights widely reported product lines; treat performance claims as vendor-/press-reported unless independently benchmarked.</p>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="HuaweiAscend"></a></p>
+<div align="center"><a href="https://www.huawei.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HUAWEI_logo.png" height="40"></a>
+&nbsp;&nbsp;
+<a href="https://www.hisilicon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HiSilicon_logo.png" height="40"></a></div>
+<div align="center"><h3>Huawei Ascend / HiSilicon</h3></div>
+
+<p><strong><a href="https://www.huawei.com/en/news/2025/9/hc-xu-keynote-speech">Huawei Atlas 900 A3 SuperPoD / Ascend 910C</a></strong></p>
+<blockquote>
+  <p>2025 — Huawei scaled Ascend 910B/910C deployments and launched Atlas 900 A3 SuperPoD (up to 384× Ascend 910C), also referred to in cloud form as CloudMatrix384-class supernodes, for large-model training and serving. Ascend 950-class follow-ons are on Huawei’s roadmap.</p>
+</blockquote>
+
+<p><a href="https://arxiv.org/abs/2506.12708">Serving Large Language Models on Huawei CloudMatrix384</a> (technical paper)</p>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="Cambricon"></a></p>
+<div align="center"><a href="https://www.cambricon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cambricon_logo.png" height="50"></a></div>
+<div align="center"><h3>Cambricon (寒武纪)</h3></div>
+
+<p><strong><a href="https://www.tomshardware.com/tech-industry/semiconductors/cambricon-targets-500000-ai-chips-in-2026-as-china-accelerates-domestic-hardware-push">Cambricon targets 500,000 AI chips in 2026</a></strong></p>
+<blockquote>
+  <p>Reports describe aggressive 2026 volume plans for Siyuan / MLU-class accelerators (including Siyuan 590 and next-gen 690 messaging), serving Chinese internet/cloud customers as a leading pure-play domestic AI-chip vendor.</p>
+</blockquote>
+
+<p><a href="https://awesomeagents.ai/hardware/cambricon-mlu590/">Cambricon MLU590 overview</a></p>
+<blockquote>
+  <p>MLU500-series cloud accelerator commonly cited for China data-center training/inference deployments with MLU-Link multi-card interconnect and Cambricon’s NeuWare / BANG software stack.</p>
+</blockquote>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="Biren"></a></p>
+<div align="center"><h3><a href="https://www.birentech.com/">Biren Technology (壁仞)</a></h3></div>
+
+<p><strong><a href="https://www.reuters.com/world/asia-pacific/china-ai-chipmaker-biren-surges-82-hong-kong-debut-kicking-off-2026-listings-2026-01-02/">China AI chipmaker Biren soars in Hong Kong debut</a></strong></p>
+<blockquote>
+  <p>Jan 2026 — Shanghai Biren Technology listed in Hong Kong (6082.HK), raising ~HK$5.58B. Best known for BR100-class GPGPU / AI accelerators positioned as high-performance domestic alternatives.</p>
+</blockquote>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="MooreThreads"></a></p>
+<div align="center"><h3><a href="https://www.mthreads.com/">Moore Threads (摩尔线程)</a></h3></div>
+
+<p><strong><a href="https://www.cnbc.com/2025/12/05/china-nvidia-moore-threads-trading-debut-1-billion-listing-ipo-shanghai-gpu-enflame-biren.html">Moore Threads surges over 400% on Shanghai trading debut</a></strong></p>
+<blockquote>
+  <p>Dec 2025 — Full-function GPU vendor (MTT S4000 / S5000 class AI cards, MUSA software stack) completed a ~$1.1B STAR Market IPO amid China’s domestic GPU push.</p>
+</blockquote>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="Enflame"></a></p>
+<div align="center"><a href="https://github.com/EnflameTechnology"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Enflame_logo.png" height="50"></a></div>
+<div align="center"><h3>Enflame (燧原)</h3></div>
+
+<p><strong><a href="https://www.hpcwire.com/off-the-wire/enflame-technology-announces-cloudblazer-with-dtu-chip-on-globalfoundries-12lp-finfet-platform-for-data-center-training/">Enflame CloudBlazer / DTU AI accelerators</a></strong></p>
+<blockquote>
+  <p>Shanghai-based accelerator vendor offering CloudBlazer training/inference products (DTU architecture) for domestic intelligent-computing centers; frequently grouped with China’s “GPU dragons” alongside Biren, Moore Threads, and MetaX. Open-source ecosystem links: <a href="https://github.com/EnflameTechnology">EnflameTechnology on GitHub</a>.</p>
+</blockquote>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="Horizon"></a></p>
+<div align="center"><a href="https://www.horizon.auto/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Horizon_Robotics_logo.png" height="50"></a></div>
+<div align="center"><h3>Horizon Robotics (地平线)</h3></div>
+
+<p><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Horizon Journey 6 / BPU Nash automotive AI processors</a></strong></p>
+<blockquote>
+  <p>Leading China ADAS / autonomous-driving compute vendor with Journey-series BPU SoCs (Journey 6 adds BPU Nash with stronger Transformer support) widely designed into passenger vehicles; focuses on efficient vision/transformer perception at the edge rather than datacenter LLM training.</p>
+</blockquote>
+
+<div align="center"><h3> </h3></div>
+
 <p><a name="AIChipCompilers"></a></p>
 
 <div align="center"><h2>AI Chip Compilers</h2></div>

From b48b844756de0ff9fe9b88c98ca37db852b4a092 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 02:09:19 +0000
Subject: [PATCH 03/13] Add MediaTek, Broadcom/OpenAI XPUs,
 MetaX/Hygon/Iluvatar
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Cover Dimensity 9400 NPU, Broadcom custom XPU partnerships and OpenAI
Jalapeño, expand China accelerator section with MetaX, Hygon DCU and
Iluvatar CoreX, and note Marvell’s hyperscaler ASIC design role.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md | 98 ++++++++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 94 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 10ae4a0..87e9475 100644
--- a/README.md
+++ b/README.md
@@ -28,9 +28,12 @@
 <li><strong>Apple</strong>: <a href="#Apple">M4 / M5</a> Apple silicon Neural Engine &amp; GPU Neural Accelerators.</li>
 <li><strong>Samsung</strong>: <a href="#Samsung">Exynos 2500</a> on-device NPU (3nm GAA).</li>
 <li><strong>Qualcomm</strong>: <a href="#Qualcomm">Snapdragon 8 Elite</a> Hexagon NPU for on-device GenAI.</li>
-<li><strong>China accelerators</strong>: new section for <a href="#China_AI_Chips">Huawei Ascend</a>, <a href="#Cambricon">Cambricon</a>, <a href="#Biren">Biren</a>, <a href="#MooreThreads">Moore Threads</a>, etc.</li>
+<li><strong>China accelerators</strong>: <a href="#China_AI_Chips">Huawei Ascend</a>, Cambricon, Biren, Moore Threads, <a href="#MetaX">MetaX</a>, <a href="#Hygon">Hygon DCU</a>, <a href="#Iluvatar">Iluvatar CoreX</a>, Enflame, Horizon.</li>
 <li><strong>Alibaba</strong>: T-Head <a href="#Alibaba">PPU / Zhenwu</a> cloud AI chips.</li>
 <li><strong>Startups</strong>: added <a href="#Etched">Etched</a> (transformer ASIC / Sohu).</li>
+<li><strong>MediaTek</strong>: <a href="#MediaTek">Dimensity 9400</a> NPU 890 / agentic on-device AI.</li>
+<li><strong>Broadcom / OpenAI</strong>: custom AI <a href="#Broadcom">XPU</a> partner; <a href="#OpenAI">Jalapeño</a> inference ASIC.</li>
+<li><strong>Marvell</strong>: noted role as custom AI ASIC partner (Trainium / Maia class programs).</li>
 <li><strong>Repo hygiene</strong>: Fixed asset links to this repository; cleaned header WeChat album links.</li>
 </ul>
 
@@ -40,10 +43,10 @@
 <HR>
 <table style="width:100%">
   <tr>
-    <th><a href="#IC_Vendors">IC Vendors</a></th><td><a href="#Intel">Intel</a>, <a href="#Qualcomm">Qualcomm</a>, <a href="#Nvidia">Nvidia</a>, <a href="#Samsung">Samsung</a>, <a href="#AMD">AMD</a>,<a href="#IBM">IBM</a>, <a href="#Marvell">Marvell</a></td>
+    <th><a href="#IC_Vendors">IC Vendors</a></th><td><a href="#Intel">Intel</a>, <a href="#Qualcomm">Qualcomm</a>, <a href="#Nvidia">Nvidia</a>, <a href="#Samsung">Samsung</a>, <a href="#AMD">AMD</a>, <a href="#MediaTek">MediaTek</a>, <a href="#IBM">IBM</a>, <a href="#Marvell">Marvell</a>, <a href="#Broadcom">Broadcom</a></td>
   </tr>
   <tr>
-    <th><a href="#Tech_Giants">Tech Giants & HPC Vendors</a></th><td><a href="#Google">Google</a>, <a href="#Amazon_AWS">Amazon_AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#Alibaba">Alibaba Group</a>, <a href="#Tencent_Cloud">Tencent Cloud</a>, <a href="#Baidu">Baidu</a>, <a href="#Fujitsu">Fujitsu</a>, <a href="#Nokia">Nokia</a>, <a href="#Facebook">Meta (Facebook)</a>, <a href="#Tesla">Tesla</a></td>
+    <th><a href="#Tech_Giants">Tech Giants & HPC Vendors</a></th><td><a href="#Google">Google</a>, <a href="#Amazon_AWS">Amazon_AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#OpenAI">OpenAI</a>, <a href="#Alibaba">Alibaba Group</a>, <a href="#Tencent_Cloud">Tencent Cloud</a>, <a href="#Baidu">Baidu</a>, <a href="#Fujitsu">Fujitsu</a>, <a href="#Nokia">Nokia</a>, <a href="#Facebook">Meta (Facebook)</a>, <a href="#Tesla">Tesla</a></td>
   </tr>
   <tr>
     <th><a href="#IP_Vendors">IP Vendors</a></th><td><a href="#ARM">ARM</a>, <a href="#Synopsys">Synopsys</a>, <a href="#Imagination">Imagination</a>, <a href="#CEVA">CEVA</a>, <a href="#Cadence">Cadence</a>, <a href="#VeriSilicon">VeriSilicon</a></td>
@@ -168,6 +171,19 @@
   <p>Earlier on-device AI messaging around Exynos 9820 / 9810-era NPU features for AR/VR and mobile deep learning.</p>
 </blockquote>   
 
+
+<div align="center"><h3> </h3></div>
+<a name="MediaTek"></a>
+<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/MediaTek_logo.png" height="40"></div>
+<div align="center"><h3> </h3></div>
+
+<p><strong><a href="https://www.mediatek.com/press-room/mediateks-dimensity-9400-flagship-soc-offers-extreme-performance-and-efficiency-for-the-latest-ai-experiences">MediaTek Dimensity 9400 — 8th Gen NPU for agentic AI</a></strong></p>
+<blockquote>
+  <p>Oct 2024 — Flagship Dimensity 9400 (TSMC 3nm) pairs an all-big-core CPU with MediaTek’s 8th-generation NPU 890. MediaTek highlights on-device LoRA training, high-quality video generation, MoE LLM support, and the Dimensity Agentic AI Engine (DAE), with claimed large gains in LLM prompt / diffusion performance vs. Dimensity 9300.</p>
+</blockquote>
+
+<p><a href="https://www.mediatek.com/products/smartphones/mediatek-dimensity-9400">Dimensity 9400 product page</a></p>
+
 <div align="center"><h3> </h3></div>
 <a name="AMD"></a>
 <div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AMD_logo.png" height="35"></div>
@@ -224,13 +240,39 @@
 
 <div align="center"><h3> </h3></div>
 
+<p><strong><a href="https://www.marvell.com/products/data-processing-units.html">Custom AI ASICs + DPUs</a></strong></p>
+<blockquote>
+  <p>Beyond OCTEON / ARMADA DPUs for networking infrastructure, Marvell is a major custom AI accelerator design partner for hyperscalers (widely reported programs include AWS Trainium/Inferentia-class and Microsoft Maia-class silicon), complementing Broadcom’s XPU franchise.</p>
+</blockquote>
+
 <p><a href="https://www.marvell.com/products/data-processing-units.html">Data Processing Units</a></p>
 <blockquote>
-  <p>Built on seven generations of the industry’s first, most scalable and widely adopted data infrastructure processors, Marvell’s OCTEON™, OCTEON™ Fusion and ARMADA® platforms are optimized for wireless infrastructure, wireline carrier networks, enterprise and cloud data centers.</p>
+  <p>Marvell’s OCTEON™, OCTEON™ Fusion and ARMADA® platforms target wireless infrastructure, carrier networks, enterprise and cloud data centers.</p>
 </blockquote>
 
 <div align="center"><h3> </h3></div>
 
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="Broadcom"></a></p>
+<div align="center"><h3>Broadcom</h3></div>
+
+<p><strong><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI and Broadcom announce strategic collaboration for custom AI accelerators</a></strong></p>
+<blockquote>
+  <p>Oct 2025 — Multi-year plan to deploy ~10 GW of OpenAI-designed accelerators co-developed with Broadcom (Ethernet scale-up/scale-out), with rack deployments targeted from H2 2026 through 2029.</p>
+</blockquote>
+
+<p><strong><a href="https://www.nasdaq.com/press-release/broadcom-announces-extended-partnership-meta-deploy-technology-support-multi">Broadcom extends partnership with Meta on MTIA custom silicon</a></strong></p>
+<blockquote>
+  <p>Apr 2026 — Broadcom’s XPU platform continues as the co-design / implementation backbone for Meta’s multi-generation MTIA accelerators and supporting Ethernet AI networking fabric.</p>
+</blockquote>
+
+<p><a href="https://www.hpcwire.com/2025/09/09/broadcom-steps-up-to-challenge-nvidia-in-ai-chip-arms-race/">Broadcom custom XPUs for hyperscaler AI</a></p>
+<blockquote>
+  <p>Broadcom co-designs customer-specific AI accelerators (XPUs) and pairs them with Tomahawk / Jericho Ethernet switching — a long-running model that began with Google TPU generations and now spans multiple frontier labs and cloud providers.</p>
+</blockquote>
+
 <div align="center"><h2><a name="Tech_Giants"></a>II. Tech Giants & HPC Vendors</h2></div>
 
 <p><HR></p>
@@ -325,6 +367,19 @@
 
 <div align="center"><h3> </h3></div>
 
+
+<p><a name="OpenAI"></a></p>
+<div align="center"><h3>OpenAI</h3></div>
+
+<p><strong><a href="https://openai.com/index/openai-broadcom-jalapeno-inference-chip/">OpenAI and Broadcom unveil Jalapeño LLM inference chip</a></strong> · <a href="https://techcrunch.com/2026/06/24/openai-unveils-its-first-custom-chip-built-by-broadcom/">TechCrunch coverage</a></p>
+<blockquote>
+  <p>Jun 2026 — OpenAI’s first custom “Intelligence Processor,” Jalapeño, is an inference-optimized ASIC co-developed with Broadcom (networking + silicon implementation) for lower-cost, higher-utilization LLM serving. First step in a multi-generation platform with initial deployments targeted by end of 2026.</p>
+</blockquote>
+
+<p><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI–Broadcom 10 GW accelerator collaboration</a></p>
+
+<div align="center"><h3> </h3></div>
+
 <p><a name="Apple"></a></p>
 
 <div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Apple_logo.png" height="60"></div>
@@ -1371,6 +1426,41 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <div align="center"><h3> </h3></div>
 
+
+<p><a name="MetaX"></a></p>
+<div align="center"><h3><a href="https://www.metax-tech.com/en/about/about.html">MetaX (沐曦)</a></h3></div>
+
+<p><strong><a href="https://www.caixinglobal.com/2026-03-26/chinese-gpu-maker-metax-doubles-revenue-amid-push-for-domestic-chips-102427800.html">Chinese GPU Maker MetaX Doubles Revenue Amid Push for Domestic Chips</a></strong></p>
+<blockquote>
+  <p>Shanghai MetaX (STAR: 688802.SH, listed Dec 2025) ships C-series training/inference GPUs (C500 mass production; C600 domestic-process follow-on) with the MXMACA software stack for AI clusters and intelligent-computing centers.</p>
+</blockquote>
+
+<p><a href="https://www.chinadaily.com.cn/a/202607/09/WS6a4f3978a310986e2b4645f5.html">MetaX scales production to meet rising AI chip demand</a></p>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="Hygon"></a></p>
+<div align="center"><h3><a href="https://www.hygon.cn/">Hygon (海光) DCU</a></h3></div>
+
+<p><strong><a href="https://www.trendforce.com/news/2026/04/29/news-huawei-ascend-cambricon-and-hygon-completed-day-0-adaptation-to-deepseek-v4/">Hygon DCU completes Day-0 adaptation for DeepSeek-V4</a></strong></p>
+<blockquote>
+  <p>Hygon’s Deep Computing Unit (DCU) GPGPU-class accelerators and DTK software stack are widely used in China for large-model training/inference; 2025–2026 coverage highlights same-day DeepSeek model adaptations alongside Ascend and Cambricon.</p>
+</blockquote>
+
+<p><a href="https://github.com/HYGON-AI/dcu-inference-cookbook">HYGON-AI DCU inference cookbook</a> (open deployment guides)</p>
+
+<div align="center"><h3> </h3></div>
+
+<p><a name="Iluvatar"></a></p>
+<div align="center"><h3><a href="https://www.iluvatar.com/">Iluvatar CoreX (天数智芯)</a></h3></div>
+
+<p><strong><a href="https://www.scmp.com/tech/big-tech/article/3341368/iluvatar-corex-targets-nvidias-rubin-gpu-road-map-amid-china-chip-push">Iluvatar CoreX targets Nvidia’s Rubin with GPU road map</a></strong></p>
+<blockquote>
+  <p>Jan 2026 — Hong Kong-listed Iluvatar CoreX outlined a multi-generation GPU architecture roadmap (Tianshu / Tianxuan / Tianji / Tianquan messaging) aiming to close the gap with Nvidia Hopper→Blackwell→Rubin class platforms, with BI-V150-class products already in AI training/inference deployments.</p>
+</blockquote>
+
+<div align="center"><h3> </h3></div>
+
 <p><a name="AIChipCompilers"></a></p>
 
 <div align="center"><h2>AI Chip Compilers</h2></div>

From 385cd77d77ad5e5c6b15ffa1104a8afeef396ce1 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 02:10:41 +0000
Subject: [PATCH 04/13] =?UTF-8?q?Add=20Anthropic=20compute=20deals,=20Soft?=
 =?UTF-8?q?Bank=E2=80=93Graphcore,=20Modalix,=20Black=20Sesame?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Document Anthropic’s multi-cloud TPU/Trainium strategy, Graphcore’s SoftBank
acquisition, SiMa.ai Modalix Physical AI platform, and Black Sesame Huashan
automotive SoCs; refresh related shortcuts and latest-updates bullets.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 51 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 87e9475..41b767a 100644
--- a/README.md
+++ b/README.md
@@ -34,6 +34,9 @@
 <li><strong>MediaTek</strong>: <a href="#MediaTek">Dimensity 9400</a> NPU 890 / agentic on-device AI.</li>
 <li><strong>Broadcom / OpenAI</strong>: custom AI <a href="#Broadcom">XPU</a> partner; <a href="#OpenAI">Jalapeño</a> inference ASIC.</li>
 <li><strong>Marvell</strong>: noted role as custom AI ASIC partner (Trainium / Maia class programs).</li>
+<li><strong>Anthropic</strong>: multi-cloud compute on <a href="#Anthropic">TPU / Trainium / GPU</a> (Google–Broadcom GW-scale deals).</li>
+<li><strong>Graphcore</strong>: <a href="#Graphcore">acquired by SoftBank</a> (2024); continues IPU / next-gen work inside SoftBank.</li>
+<li><strong>Edge / ADAS</strong>: <a href="#SiMa">SiMa Modalix</a> Physical AI; <a href="#BlackSesame">Black Sesame Huashan</a> automotive SoCs.</li>
 <li><strong>Repo hygiene</strong>: Fixed asset links to this repository; cleaned header WeChat album links.</li>
 </ul>
 
@@ -46,7 +49,7 @@
     <th><a href="#IC_Vendors">IC Vendors</a></th><td><a href="#Intel">Intel</a>, <a href="#Qualcomm">Qualcomm</a>, <a href="#Nvidia">Nvidia</a>, <a href="#Samsung">Samsung</a>, <a href="#AMD">AMD</a>, <a href="#MediaTek">MediaTek</a>, <a href="#IBM">IBM</a>, <a href="#Marvell">Marvell</a>, <a href="#Broadcom">Broadcom</a></td>
   </tr>
   <tr>
-    <th><a href="#Tech_Giants">Tech Giants & HPC Vendors</a></th><td><a href="#Google">Google</a>, <a href="#Amazon_AWS">Amazon_AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#OpenAI">OpenAI</a>, <a href="#Alibaba">Alibaba Group</a>, <a href="#Tencent_Cloud">Tencent Cloud</a>, <a href="#Baidu">Baidu</a>, <a href="#Fujitsu">Fujitsu</a>, <a href="#Nokia">Nokia</a>, <a href="#Facebook">Meta (Facebook)</a>, <a href="#Tesla">Tesla</a></td>
+    <th><a href="#Tech_Giants">Tech Giants & HPC Vendors</a></th><td><a href="#Google">Google</a>, <a href="#Amazon_AWS">Amazon_AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#OpenAI">OpenAI</a>, <a href="#Anthropic">Anthropic</a>, <a href="#Alibaba">Alibaba Group</a>, <a href="#Tencent_Cloud">Tencent Cloud</a>, <a href="#Baidu">Baidu</a>, <a href="#Fujitsu">Fujitsu</a>, <a href="#Nokia">Nokia</a>, <a href="#Facebook">Meta (Facebook)</a>, <a href="#Tesla">Tesla</a></td>
   </tr>
   <tr>
     <th><a href="#IP_Vendors">IP Vendors</a></th><td><a href="#ARM">ARM</a>, <a href="#Synopsys">Synopsys</a>, <a href="#Imagination">Imagination</a>, <a href="#CEVA">CEVA</a>, <a href="#Cadence">Cadence</a>, <a href="#VeriSilicon">VeriSilicon</a></td>
@@ -57,7 +60,7 @@
   </tr>
   <tr>
     <th><a href="#China_AI_Chips">China AI Accelerators</a></th>
-    <td><a href="#HuaweiAscend">Huawei Ascend</a>, <a href="#Cambricon">Cambricon</a>, <a href="#Biren">Biren</a>, <a href="#MooreThreads">Moore Threads</a>, <a href="#Enflame">Enflame</a>, <a href="#Horizon">Horizon Robotics</a>, <a href="#Alibaba">Alibaba T-Head</a>, <a href="#Baidu">Baidu Kunlun</a></td>
+    <td><a href="#HuaweiAscend">Huawei Ascend</a>, <a href="#Cambricon">Cambricon</a>, <a href="#Biren">Biren</a>, <a href="#MooreThreads">Moore Threads</a>, <a href="#MetaX">MetaX</a>, <a href="#Hygon">Hygon</a>, <a href="#Iluvatar">Iluvatar CoreX</a>, <a href="#Enflame">Enflame</a>, <a href="#Horizon">Horizon Robotics</a>, <a href="#BlackSesame">Black Sesame</a>, <a href="#Alibaba">Alibaba T-Head</a>, <a href="#Baidu">Baidu Kunlun</a></td>
   </tr>
 </table>
 
@@ -380,6 +383,20 @@
 
 <div align="center"><h3> </h3></div>
 
+
+<p><a name="Anthropic"></a></p>
+<div align="center"><h3>Anthropic</h3></div>
+
+<p><strong><a href="https://www.anthropic.com/news/google-broadcom-partnership-compute">Anthropic expands partnership with Google and Broadcom for next-generation compute</a></strong></p>
+<blockquote>
+  <p>Apr 2026 — Expanded agreement for multiple gigawatts of next-generation Google TPU capacity (Broadcom SEC filings cite ~3.5 GW), expected online from 2027, on top of prior &gt;1 GW TPU commitments. Anthropic emphasizes a multi-hardware strategy: AWS Trainium (incl. Project Rainier), Google TPUs, and NVIDIA GPUs.</p>
+</blockquote>
+
+<p><a href="https://techcrunch.com/2026/04/07/anthropic-compute-deal-google-broadcom-tpus/">TechCrunch: Anthropic ups compute deal with Google and Broadcom</a> ·
+<a href="https://www.cnbc.com/2025/10/23/anthropic-google-cloud-deal-tpu.html">CNBC: Oct 2025 Google TPU cloud deal</a></p>
+
+<div align="center"><h3> </h3></div>
+
 <p><a name="Apple"></a></p>
 
 <div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Apple_logo.png" height="60"></div>
@@ -651,7 +668,14 @@
 
 <div align="center"><a href="https://www.graphcore.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Graphcore_logo.png" height="70"></a></div>
 
-<p><strong><a href="https://www.eetimes.com/graphcore-supercharges-ipu-with-wafer-on-wafer/">Graphcore Supercharges IPU with Wafer-on-Wafer</a></strong></p>
+<p><strong><a href="https://www.graphcore.ai/posts/graphcore-joins-softbank-group-to-build-next-generation-of-ai-compute">Graphcore joins SoftBank Group to build next generation of AI compute</a></strong></p>
+<blockquote>
+  <p>Jul 2024 — SoftBank acquired Graphcore as a wholly owned subsidiary (terms undisclosed; press estimates ~$500M). Graphcore continues under its own brand, with SoftBank backing next-generation IPU / AI compute development (including SoftBank-ecosystem follow-on investment reporting into 2026).</p>
+</blockquote>
+
+<p><a href="https://www.reuters.com/technology/artificial-intelligence/japans-softbank-acquires-british-ai-chipmaker-graphcore-2024-07-11/">Reuters: SoftBank acquires Graphcore</a></p>
+
+<p><strong><a href="https://www.eetimes.com/graphcore-supercharges-ipu-with-wafer-on-wafer/">Graphcore Supercharges IPU with Wafer-on-Wafer</a></strong> (historical)</p>
 <blockquote>
   <p>Graphcore unveiled its third-generation intelligence processing unit (IPU), the first processor to be built using 3D wafer-on-wafer (WoW) technology.</p>
 </blockquote>
@@ -1099,11 +1123,19 @@
 
 <p><a name="SiMa"></a></p>
 
-<div align="center"><a href="http://sima.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SiMa_logo.png" height="40"></a></div>
+<div align="center"><a href="https://sima.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SiMa_logo.png" height="40"></a></div>
 
 <div align="center"><h3> </h3></div>
 
-<p><strong><a href="https://www.businesswire.com/news/home/20200512005313/en/SiMa.ai-Raises-30-Million-Series-Investment-Led">SiMa.ai Raises $30 Million in Series A Investment Round Led by Dell Technologies Capital</a></strong></p>
+<p><strong><a href="https://sima.ai/press-release/sima-ai-next-gen-platform-for-physical-ai-in-production/">SiMa.ai Modalix platform for Physical AI in production</a></strong></p>
+<blockquote>
+  <p>Aug 2025 — Second-generation MLSoC Modalix ships for edge / Physical AI (robots, industrial, automotive, vision), targeting CNNs + Transformers/LLMs/GenAI under ~10W, with SoM/devkits and the LLiMa framework for on-device LLM deployment.</p>
+</blockquote>
+
+<p><a href="https://sima.ai/press-release/sima-ai-expands-one-platform-for-edge-ai-with-mlsoc-modalix/">MLSoC Modalix product family announcement</a> (2024) ·
+<a href="https://sima.ai/press-release/sima-ai-launches-modalix-pcie-card-for-industrial-edge-ai-llms/">Modalix PCIe card for industrial edge LLMs</a> (2026)</p>
+
+<p><a href="https://www.businesswire.com/news/home/20200512005313/en/SiMa.ai-Raises-30-Million-Series-Investment-Led">SiMa.ai Raises $30 Million in Series A</a> (historical)</p>
 <blockquote>
   <p>SAN JOSE, Calif.--(BUSINESS WIRE)--SiMa.ai, the company enabling high performance machine learning to go green, today announced its Machine Learning SoC (MLSoC) platform – the industry’s first unified solution to support traditional compute with high performance, lowest power, safe and secure machine learning inference. Delivering the highest frames per second per watt, SiMa.ai’s MLSoC is the first machine learning platform to break the 1000 FPS/W barrier for ResNet-501. In customer engagements, the company has demonstrated 10-30x improvement in FPS/W through its automated software flow across a wide range of embedded edge applications, over today’s competing solutions. The platform will provide machine learning solutions that range from 50 TOPs@5W to 200 TOPs@20W, delivering an industry first of 10 TOPs/W for high performance inference.</p>
 </blockquote>
@@ -1427,6 +1459,20 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 <div align="center"><h3> </h3></div>
 
 
+<p><a name="BlackSesame"></a></p>
+<div align="center"><a href="https://www.blacksesame.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blacksesame_logo.png" height="50"></a></div>
+<div align="center"><h3>Black Sesame Technologies (黑芝麻智能)</h3></div>
+
+<p><strong><a href="https://www.blacksesame.com/en/huashan-a1000/">Huashan A1000 automotive-grade ADAS / AD SoCs</a></strong></p>
+<blockquote>
+  <p>Hong Kong-listed (02533.HK, Aug 2024) automotive AI SoC vendor. Huashan A1000-family chips target L2+/L3 assisted driving with automotive-grade safety certifications and are reported in mass-production vehicle models; Huashan A2000 messaging extends toward higher-performance / cross-domain AI inference.</p>
+</blockquote>
+
+<p><a href="https://www.caixinglobal.com/2024-08-01/chinese-auto-chip-maker-black-sesame-to-raise-143-million-in-hong-kong-ipo-102222110.html">Caixin: Black Sesame Hong Kong IPO</a></p>
+
+<div align="center"><h3> </h3></div>
+
+
 <p><a name="MetaX"></a></p>
 <div align="center"><h3><a href="https://www.metax-tech.com/en/about/about.html">MetaX (沐曦)</a></h3></div>
 

From 4ffe7bf8eed22d2eb21d822d40ee7d8bd091f8dd Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 02:16:40 +0000
Subject: [PATCH 05/13] Polish Pages UX and refresh Arm/Mythic/SambaNova
 coverage

Remove empty spacer divs, normalize vendor logo markup and alt text,
add custom modernist CSS overlays, and update Arm Ethos-U85, Mythic
Series D/Videantis, and SambaNova SN50 entries with current sources.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md             | 598 +++++++++++++-----------------------------
 _config.yml           |   7 +-
 assets/css/style.scss | 255 ++++++++++++++++++
 3 files changed, 443 insertions(+), 417 deletions(-)
 create mode 100644 assets/css/style.scss

diff --git a/README.md b/README.md
index 41b767a..338c451 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,19 @@
-<div align="center"><h1>AI Chip (ICs and IPs)</h1></div>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI-chips.png"></div>
-<br>
-<div align="center">Editor <a href="https://www.linkedin.com/in/shan-tang-27342510/"><strong>S.T.</strong></a> (LinkedIn)</div>
-<div align="center"><strong>Welcome to My WeChat Blog <a href="https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&amp;__biz=MzI3MDQ2MjA3OA==&amp;scene=1&amp;album_id=1374108991751782402&amp;count=3#wechat_redirect">StarryHeavensAbove</a> for more AI chip related articles</strong></div>
-<div align="center"><strong>欢迎访问我的微信公众号 <a href="https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&amp;__biz=MzI3MDQ2MjA3OA==&amp;scene=1&amp;album_id=1374108991751782402&amp;count=3#wechat_redirect">StarryHeavensAbove</a></strong></div>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/qrcode_for_weichat_258.jpg" height="100"></div>
-<div align="center"><h1> </h1></div>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI_Chip_Landscape_v0p7.png"></div>
-<div align="center"><h1> </h1></div>
+<header class="page-header" align="center">
+<h1 class="site-brand">AI Chip (ICs and IPs)</h1>
+<div class="hero-art"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI-chips.png" loading="lazy" alt="AI chips illustration" /></div>
+<p class="editor-line">Editor <a href="https://www.linkedin.com/in/shan-tang-27342510/"><strong>S.T.</strong></a> (LinkedIn)</p>
+<p class="wechat-line"><strong>WeChat / 微信公众号</strong> · <a href="https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&amp;__biz=MzI3MDQ2MjA3OA==&amp;scene=1&amp;album_id=1374108991751782402&amp;count=3#wechat_redirect">StarryHeavensAbove</a> — AI chip articles</p>
+<div class="wechat-qr"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/qrcode_for_weichat_258.jpg" height="100" loading="lazy" alt="WeChat QR code" /></div>
+<div class="landscape-map"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI_Chip_Landscape_v0p7.png" loading="lazy" alt="AI Chip Landscape map" /></div>
+<p class="page-intro">A living directory of AI accelerators (ICs &amp; IPs) — hyperscaler custom silicon, GPU/NPU vendors, IP houses, startups, and China domestic accelerators. Prefer official / primary sources; performance claims are often vendor-reported.</p>
+</header>
+
  
-<div align="center"><h2>Latest updates</h2></div>
-<p align="center"><em>Last refreshed: July 2026 — major vendor coverage updated for the generative-AI / LLM accelerator era.</em></p>
-<HR>
+<div align="center" class="section-title"><h2>Latest updates</h2></div>
+<p class="refresh-note" align="center"><em>Last refreshed: July 2026 — major vendor coverage updated for the generative-AI / LLM accelerator era.</em></p>
+<hr class="section-rule" />
 
-<ul>
+<ul class="updates-list">
 <li><strong>NVIDIA</strong>: <a href="#Nvidia">Vera Rubin</a> platform (CES/GTC 2026) and <a href="#Nvidia">Blackwell</a> (B200 / GB200 NVL72).</li>
 <li><strong>AMD</strong>: <a href="#AMD">Instinct MI350</a> series (CDNA 4) and Meta partnership for Helios / MI450-class GPUs.</li>
 <li><strong>Intel</strong>: <a href="#Habana">Gaudi 3</a> AI accelerator for enterprise GenAI training and inference.</li>
@@ -37,14 +37,18 @@
 <li><strong>Anthropic</strong>: multi-cloud compute on <a href="#Anthropic">TPU / Trainium / GPU</a> (Google–Broadcom GW-scale deals).</li>
 <li><strong>Graphcore</strong>: <a href="#Graphcore">acquired by SoftBank</a> (2024); continues IPU / next-gen work inside SoftBank.</li>
 <li><strong>Edge / ADAS</strong>: <a href="#SiMa">SiMa Modalix</a> Physical AI; <a href="#BlackSesame">Black Sesame Huashan</a> automotive SoCs.</li>
-<li><strong>Repo hygiene</strong>: Fixed asset links to this repository; cleaned header WeChat album links.</li>
+<li><strong>Tesla</strong>: <a href="#Tesla">AI5 tape-out</a> (2026); Dojo / AI4 lineage retained as historical context.</li>
+<li><strong>FuriosaAI</strong>: <a href="#Furiosa">RNGD</a> mass production + Broadcom next-gen partnership; Equinix Europe deploy.</li>
+<li><strong>Hailo</strong>: <a href="#Hailo">Hailo-10H</a> edge GenAI accelerator (LLM/VLM) generally available.</li>
+<li><strong>Arm</strong>: <a href="#ARM">Ethos-U85</a> micro-NPU with native transformer support (edge / IoT GenAI).</li>
+<li><strong>SambaNova</strong>: <a href="#SambaNova">SN50 RDU</a> agentic inference chip + Intel collaboration / Series E.</li>
+<li><strong>Mythic</strong>: <a href="#Mythic">$125M Series D</a> + Videantis acquisition for hybrid analog/digital AI platform.</li>
+<li><strong>Pages UX</strong>: HTML structure cleanup + custom CSS on GitHub Pages (section chrome, shortcut table, vendor logo blocks).</li>
 </ul>
 
-<div align="center"><h1> </h1></div>
-
-<div align="center"><h2>Shortcut</h2></div>
-<HR>
-<table style="width:100%">
+<div align="center" class="section-title"><h2>Shortcut</h2></div>
+<hr class="section-rule" />
+<table class="shortcut-table">
   <tr>
     <th><a href="#IC_Vendors">IC Vendors</a></th><td><a href="#Intel">Intel</a>, <a href="#Qualcomm">Qualcomm</a>, <a href="#Nvidia">Nvidia</a>, <a href="#Samsung">Samsung</a>, <a href="#AMD">AMD</a>, <a href="#MediaTek">MediaTek</a>, <a href="#IBM">IBM</a>, <a href="#Marvell">Marvell</a>, <a href="#Broadcom">Broadcom</a></td>
   </tr>
@@ -56,7 +60,7 @@
   </tr>
   <tr>  
     <th><a href="#Startups">Startups</a></th>
-    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Etched">Etched</a>, <a href="#Groq">Groq</a>, <a href="#SambaNova">SambaNova</a>, <a href="#d-matrix">d-Matrix</a>, <a href="#Furiosa">Furiosa AI</a>, … (full list in section IV)</td>
+    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Etched">Etched</a>, <a href="#Groq">Groq</a>, <a href="#SambaNova">SambaNova</a>, <a href="#Mythic">Mythic</a>, <a href="#Hailo">Hailo</a>, <a href="#Furiosa">FuriosaAI</a>, <a href="#d-matrix">d-Matrix</a>, <a href="#SiMa">SiMa</a>, … (full list in section IV)</td>
   </tr>
   <tr>
     <th><a href="#China_AI_Chips">China AI Accelerators</a></th>
@@ -64,16 +68,11 @@
   </tr>
 </table>
 
-<div align="center"><h1> </h1></div>
+<div align="center" class="section-title"><h2><a name="IC_Vendors"></a>I. IC Vendors</h2></div>
+<hr class="section-rule" />
 
-<div align="center"><h2><a name="IC_Vendors"></a>I. IC Vendors</h2></div>
-<HR>
-<div align="center"><h1> </h1></div>
-
-<div align="center"><h3> </h3></div>
 <a name="Nvidia"></a>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nvidia_logo.png" height="50"> </div>
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nvidia_logo.png" height="50" loading="lazy" alt="NVIDIA" /> </div>
 <div align="center"><h3>GPU</h3></div>
 
 <p><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Kicks Off the Next Generation of AI With Rubin</a></strong></p>
@@ -101,11 +100,8 @@
   <p>Kicking off another busy Spring GPU Technology Conference for NVIDIA, this morning the graphics and accelerator designer is announcing that they are going to once again design their own Arm-based CPU/SoC. Dubbed Grace – after Grace Hopper, the computer programming pioneer and US Navy rear admiral – the CPU is NVIDIA’s latest stab at more fully vertically integrating their hardware stack by being able to offer a high-performance CPU alongside their regular GPU wares. According to NVIDIA, the chip is being designed specifically for large-scale neural network workloads, and is expected to become available in NVIDIA products in 2023.</p>
 </blockquote>
 
-
-<div align="center"><h3> </h3></div>
 <a name="Intel"></a>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Intel_logo.png" height="60"></div>
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Intel_logo.png" height="60" loading="lazy" alt="Intel" /></div>
 
 <a name="Mobileye"></a>
 <div align="center"><h3>Mobileye EyeQ</h3></div>
@@ -135,10 +131,8 @@
   <p>The primary motivation to create this new training instance class was presented by Andy Jassy in the 2020 re:Invent: “To provide our end-customers with up to 40% better price-performance than the current generation of GPU-based instances.”</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
 <a name="Qualcomm"></a>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Qualcomm_logo.png" height="40"></div>
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Qualcomm_logo.png" height="40" loading="lazy" alt="Qualcomm" /></div>
 
 <p><strong><a href="https://www.androidauthority.com/snapdragon-8-elite-3491805/">Snapdragon 8 Elite — Hexagon NPU for on-device GenAI</a></strong></p>
 <blockquote>
@@ -160,10 +154,8 @@
   <p>The Qualcomm Cloud AI 100, designed for AI inference acceleration, addresses unique requirements in the cloud, including power efficiency, scale, process node advancements, and signal processing—facilitating the ability of datacenters to run inference on the edge cloud faster and more efficiently. Qualcomm Cloud AI 100 is designed to be a leading solution for datacenters who increasingly rely on infrastructure at the edge-cloud.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
 <a name="Samsung"></a>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Samsung_logo.png" height="35"></div>
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Samsung_logo.png" height="35" loading="lazy" alt="Samsung" /></div>
 <p><strong><a href="https://semiconductor.samsung.com/processor/mobile-processor/exynos-2500/">Samsung Exynos 2500</a></strong></p>
 <blockquote>
   <p>Flagship mobile SoC on Samsung 3nm GAA process with a substantially upgraded NPU (Samsung cites up to ~59 TOPS and ~39% better on-device AI vs. Exynos 2400) for generative AI and privacy-preserving on-device inference.</p>
@@ -174,11 +166,8 @@
   <p>Earlier on-device AI messaging around Exynos 9820 / 9810-era NPU features for AR/VR and mobile deep learning.</p>
 </blockquote>   
 
-
-<div align="center"><h3> </h3></div>
 <a name="MediaTek"></a>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/MediaTek_logo.png" height="40"></div>
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/MediaTek_logo.png" height="40" loading="lazy" alt="MediaTek" /></div>
 
 <p><strong><a href="https://www.mediatek.com/press-room/mediateks-dimensity-9400-flagship-soc-offers-extreme-performance-and-efficiency-for-the-latest-ai-experiences">MediaTek Dimensity 9400 — 8th Gen NPU for agentic AI</a></strong></p>
 <blockquote>
@@ -187,10 +176,8 @@
 
 <p><a href="https://www.mediatek.com/products/smartphones/mediatek-dimensity-9400">Dimensity 9400 product page</a></p>
 
-<div align="center"><h3> </h3></div>
 <a name="AMD"></a>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AMD_logo.png" height="35"></div>
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AMD_logo.png" height="35" loading="lazy" alt="AMD" /></div>
 <p><strong><a href="https://www.amd.com/en/products/accelerators/instinct/mi350.html">AMD Instinct™ MI350 Series GPUs</a></strong></p>
 <blockquote>
   <p>Built on 4th Gen AMD CDNA™ architecture, MI350X / MI355X deliver large HBM3E capacity (up to 288GB) and high bandwidth for generative AI training and inference, with expanded low-precision datatype support (including MXFP6 / MXFP4). MI400-series / Helios rack-scale systems are on AMD’s 2026 roadmap.</p>
@@ -208,9 +195,7 @@
 
 <p><a name="IBM"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/IBM_logo.png" height="40"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/IBM_logo.png" height="40" loading="lazy" alt="IBM" /></div>
 
 <p><a href="https://www.ibm.com/blogs/systems/ibm-telum-processor-the-next-gen-microprocessor-for-ibm-z-and-ibm-linuxone/">Meet the IBM Artificial Intelligence Unit</a></p>
 
@@ -235,13 +220,9 @@
   <p>"The IBM Research AI Hardware Center is a global research hub headquartered in Albany, New York. The center is focused on enabling next-generation chips and systems that support the tremendous processing power and unprecedented speed that AI requires to realize its full potential.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Marvell"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Marvell_logo.png" height="60"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Marvell_logo.png" height="60" loading="lazy" alt="Marvell" /></div>
 
 <p><strong><a href="https://www.marvell.com/products/data-processing-units.html">Custom AI ASICs + DPUs</a></strong></p>
 <blockquote>
@@ -253,11 +234,6 @@
   <p>Marvell’s OCTEON™, OCTEON™ Fusion and ARMADA® platforms target wireless infrastructure, carrier networks, enterprise and cloud data centers.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
-
-<div align="center"><h3> </h3></div>
-
 <p><a name="Broadcom"></a></p>
 <div align="center"><h3>Broadcom</h3></div>
 
@@ -276,17 +252,13 @@
   <p>Broadcom co-designs customer-specific AI accelerators (XPUs) and pairs them with Tomahawk / Jericho Ethernet switching — a long-running model that began with Google TPU generations and now spans multiple frontier labs and cloud providers.</p>
 </blockquote>
 
-<div align="center"><h2><a name="Tech_Giants"></a>II. Tech Giants & HPC Vendors</h2></div>
+<div align="center" class="section-title"><h2><a name="Tech_Giants"></a>II. Tech Giants & HPC Vendors</h2></div>
 
-<p><HR></p>
-
-<div align="center"><h3> </h3></div>
+<hr class="section-rule" />
 
 <p><a name="Google"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Google_logo.png" height="40"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Google_logo.png" height="40" loading="lazy" alt="Google" /></div>
 
 <p><strong><a href="https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus">Introducing Trillium, sixth-generation TPUs</a></strong></p>
 <blockquote>
@@ -327,13 +299,9 @@
 <a href="https://mp.weixin.qq.com/s/g-BDlvSy-cx4AKItcWF7jQ">脉动阵列 - 因Google TPU获得新生</a><br><br>
 <a href="https://www.linkedin.com/pulse/should-we-all-embrace-systolic-arrays-chien-ping-lu">Should We All Embrace Systolic Arrays?</a><br></p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Amazon_AWS"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Amazon_AWS.png" height="50"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Amazon_AWS.png" height="50" loading="lazy" alt="Amazon AWS" /></div>
 
 <p><strong><a href="https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/">Amazon EC2 Trn2 Instances and Trn2 UltraServers</a></strong></p>
 <blockquote>
@@ -355,22 +323,15 @@
   <p>AWS Inferentia provides high throughput, low latency inference performance at an extremely low cost. Each chip provides hundreds of TOPS (tera operations per second) of inference throughput to allow complex models to make fast predictions. For even more performance, multiple AWS Inferentia chips can be used together to drive thousands of TOPS of throughput. AWS Inferentia will be available for use with Amazon SageMaker, Amazon EC2, and Amazon Elastic Inference.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Microsoft"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Microsoft_logo.png" height="60"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Microsoft_logo.png" height="60" loading="lazy" alt="Microsoft" /></div>
 
 <p><strong><a href="https://azure.microsoft.com/en-us/blog/azure-maia-for-the-era-of-ai-from-silicon-to-software-to-systems/">Azure Maia for the era of AI: From silicon to software to systems</a></strong></p>
 <blockquote>
   <p>Microsoft’s first in-house AI accelerator, Azure Maia 100 (5nm, advanced packaging), is co-designed for Azure AI / Copilot-class cloud workloads, with a software stack integrating PyTorch and ONNX Runtime.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
-
 <p><a name="OpenAI"></a></p>
 <div align="center"><h3>OpenAI</h3></div>
 
@@ -381,9 +342,6 @@
 
 <p><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI–Broadcom 10 GW accelerator collaboration</a></p>
 
-<div align="center"><h3> </h3></div>
-
-
 <p><a name="Anthropic"></a></p>
 <div align="center"><h3>Anthropic</h3></div>
 
@@ -395,13 +353,9 @@
 <p><a href="https://techcrunch.com/2026/04/07/anthropic-compute-deal-google-broadcom-tpus/">TechCrunch: Anthropic ups compute deal with Google and Broadcom</a> ·
 <a href="https://www.cnbc.com/2025/10/23/anthropic-google-cloud-deal-tpu.html">CNBC: Oct 2025 Google TPU cloud deal</a></p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Apple"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Apple_logo.png" height="60"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Apple_logo.png" height="60" loading="lazy" alt="Apple" /></div>
 
 <p><strong><a href="https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/">Apple unleashes M5, the next big leap in AI performance for Apple silicon</a></strong></p>
 <blockquote>
@@ -418,13 +372,9 @@
   <p>May 2024 — M4 Neural Engine up to ~38 TOPS, with CPU ML accelerators and high-bandwidth unified memory for on-device AI on iPad Pro and later Macs.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Alibaba"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/alibaba_logo.png" height="60"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/alibaba_logo.png" height="60" loading="lazy" alt="Alibaba" /></div>
 
 <p><strong><a href="https://www.eetimes.com/alibaba-unveils-own-ai-chip-mounting-direct-challenge-to-nvidia/">Alibaba Unveils Own AI Chip, Mounting Direct Challenge to Nvidia</a></strong></p>
 <blockquote>
@@ -441,26 +391,19 @@
   <p>Pingtouge / T-Head’s first AI inference ASIC, highlighted for high images-per-second inference throughput at Apsara Conference 2019.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Tencent_Cloud"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tencent_Cloud_logo.png" height="30"></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tencent_Cloud_logo.png" height="30" loading="lazy" alt="Tencent Cloud" /></div>
 
-<div align="center"><h3> </h3></div>
 <p><a href="https://www.datacenterdynamics.com/en/news/tencent-reveals-three-data-center-chips-for-ai-video-transcoding-and-networking/">Tencent reveals three data center chips - for AI, video transcoding, and networking</a></p>
 <blockquote>
   <p>The company claims that the Zixiao AI chip is twice as good as comparable competing products, video transcoding chip Canghai was 30 percent better, and SmartNIC Xuanling was apparently four times as good. It did not provide external benchmarks or specific product details.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><br />
 <a name="Baidu"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Baidu_logo.png" height="40"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Baidu_logo.png" height="40" loading="lazy" alt="Baidu" /></div>
 
 <p><strong><a href="https://www.reuters.com/technology/baidu-says-2nd-gen-kunlun-ai-chips-enter-mass-production-2021-08-18/">Baidu Kunlun / Kunlunxin AI accelerators</a></strong></p>
 <blockquote>
@@ -472,38 +415,26 @@
   <p>Early mass-production milestone for Baidu’s second-generation Kunlun AI chips.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Fujitsu"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Fujitsu_logo.png" height="40"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Fujitsu_logo.png" height="40" loading="lazy" alt="Fujitsu" /></div>
 
 <blockquote>
   <p>This <a href="https://www.nextplatform.com/2017/08/09/fujitsu-bets-deep-leaning-hpc-divergence/">DLU that Fujitsu is creating</a> is done from scratch, and it is not based on either the Sparc or ARM instruction set and, in fact, it has its own instruction set and a new data format specifically for deep learning, which were created from scratch. 
   Japanese computing giant Fujitsu. Which knows a thing or two about making a very efficient and highly scalable system for HPC workloads, as evidenced by the K supercomputer, does not believe that the HPC and AI architectures will converge. Rather, the company is banking on the fact that these architectures will diverge and will require very specialized functions. </p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Nokia"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nokia_logo.png" height="30"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nokia_logo.png" height="30" loading="lazy" alt="Nokia" /></div>
 
 <blockquote>
   <p>Nokia has developed the <a href="https://networks.nokia.com/5g/reefshark">ReefShark chipsets</a> for its 5G network solutions. AI is implemented in the ReefShark design for radio and embedded in the baseband to use augmented deep learning to trigger smart, rapid actions by the autonomous, cognitive network, enhancing network optimization and increasing business opportunities.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Facebook"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/facebook_logo.png" height="50"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/facebook_logo.png" height="50" loading="lazy" alt="Meta" /></div>
 
 <p><strong><a href="https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/">Our next generation Meta Training and Inference Accelerator (MTIA)</a></strong></p>
 <blockquote>
@@ -520,15 +451,18 @@
   <p>Early reporting on Meta/Facebook’s in-house ML chip efforts for recommendation workloads.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Tesla"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tesla_logo.png" height="60"></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tesla_logo.png" height="60" loading="lazy" alt="Tesla" /></div>
 
-<div align="center"><h3> </h3></div>
+<p><strong><a href="https://electrek.co/2026/04/15/tesla-ai5-chip-taped-out-musk-ai6-dojo3/">Tesla AI5 chip taped out</a></strong></p>
+<blockquote>
+  <p>Apr 2026 — Elon Musk announced Tesla’s next-generation AI5 inference/training SoC has taped out, with AI6 / Dojo3 also mentioned as in progress. Volume production and broad vehicle deployment remain gated (reports point to mid–late 2027+); near-term vehicles continue on AI4 / interim upgrades, while AI5 is also positioned for Optimus and internal clusters.</p>
+</blockquote>
+
+<p><a href="https://www.trendforce.com/news/2026/07/13/news-samsung-reportedly-completes-tesla-ai5-tape-out-paving-the-way-for-2nm-ramp-at-taylor-fab/">TrendForce: Samsung foundry path for AI5 / Taylor 2nm messaging</a></p>
 
-<p><strong><a href="https://www.forbes.com/sites/jamesmorris/2022/10/06/teslas-biggest-news-at-ai-day-was-the-dojo-supercomputer-not-the-optimus-robot/">Tesla’s Biggest News At AI Day Was The Dojo Supercomputer, Not The Optimus Robot</a></strong></p>
+<p><strong><a href="https://www.forbes.com/sites/jamesmorris/2022/10/06/teslas-biggest-news-at-ai-day-was-the-dojo-supercomputer-not-the-optimus-robot/">Tesla AI Day — Dojo supercomputer</a></strong> (historical)</p>
 <blockquote>
   <p>Elon Musk played AI Day to the crowd with the focus on the Optimus humanoid robot. But while this could have a huge impact on our lives and society if it does enter mass production at the price Musk suggested ($20,000), another part of the presentation will have more immediate effects. That was the status report on the Dojo supercomputer. It could really change the world much more quickly than a bipedal bot.</p>
 </blockquote>
@@ -538,99 +472,75 @@
   <p>Tesla hosted their AI Day and revealed the innerworkings of their software and hardware infrastructure. Part of this reveal was the previously teased Dojo AI training chip. Tesla claims their D1 Dojo chip has a GPU level compute, CPU level flexibility, with networking switch IO. </p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
-<div align="center"><h2><a name="IP_Vendors"></a>III. Traditional IP Vendors</h2></div>
-
-<p><HR></p>
+<div align="center" class="section-title"><h2><a name="IP_Vendors"></a>III. Traditional IP Vendors</h2></div>
 
-<div align="center"><h3> </h3></div>
+<hr class="section-rule" />
 
 <p><a name="ARM"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ARM_logo.png" height="30"></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ARM_logo.png" height="30" loading="lazy" alt="Arm" /></div>
 
-<div align="center"><h3> </h3></div>
-<a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-n78">NPU ETHOS-N78</a></p>
+<p><strong><a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85">Arm Ethos-U85</a></strong> — third-generation Ethos-U micro-NPU</p>
 <blockquote>
-  <p>Specifically designed for inference at the edge, the ML processor gives an industry-leading performance of 4.6 TOPs, with a stunning efficiency of 3 TOPs/W for mobile devices and smart IP cameras.</p>
+  <p>Scales from 128–2048 MACs (up to ~4 TOPS @ 1 GHz) with native transformer operator support for edge / IoT generative and vision models; ~20% more energy efficient than Ethos-U65 and pairs with Cortex-M or Cortex-A hosts (Corstone-320 reference). Same Ethos toolchain / Vela compiler path as prior U-series.</p>
 </blockquote>
 
-<p><strong><a href="https://www.anandtech.com/show/12791/arm-details-project-trillium-mlp-architecture">ARM Details "Project Trillium" Machine Learning Processor Architecture</a></strong></p>
+<p><a href="https://newsroom.arm.com/news/iot-reference-design-platform-2024">Arm newsroom: Ethos-U85 + Corstone-320 IoT reference platform</a></p>
+
+<p><strong><a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-n78">Ethos-N78</a></strong> (legacy application-class NPU)</p>
 <blockquote>
-  <p>Arm’s second-generation, highly scalable and efficient NPU, the Ethos-N78 enables new immersive applications with a 2.5x increase in single-core performance now scalable from 1 to 10 TOP/s and beyond through many-core technologies. It provides flexibility to optimize the ML capability with 90+ configurations.</p>
+  <p>Second-generation Ethos-N series for immersive edge inference; single-core performance scalable from ~1 to 10+ TOPS via many-core configurations (historical product line still referenced in many SoCs).</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
+<p><a href="https://www.anandtech.com/show/12791/arm-details-project-trillium-mlp-architecture">ARM Details "Project Trillium" Machine Learning Processor Architecture</a> (historical)</p>
 
 <p><a name="Synopsys"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Synopsys_logo.png" height="40"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Synopsys_logo.png" height="40" loading="lazy" alt="Synopsys" /></div>
 
 <p><strong><a href="https://news.synopsys.com/2022-04-19-Synopsys-Introduces-Industrys-Highest-Performance-Neural-Processor-IP">Synopsys Introduces Industry's Highest Performance Neural Processor IP</a></strong></p>
 <blockquote>
   <p>New DesignWare ARC NPX6 NPU IP Delivers Up to 3,500 TOPS Performance for Automotive, Consumer and Data Center Chip Designs</p>
 </blockquote>
 
-
-<div align="center"><h3> </h3></div>
-
 <p><a name="Imagination"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Imagination_logo.png" height="60"></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Imagination_logo.png" height="60" loading="lazy" alt="Imagination" /></div>
 
 <p><a href="https://www.imaginationtech.com/products/ai/">AI Processors</a></p>
 <blockquote>
   <p>Whether you want smartness residing in the palm of your hand, consumer products or industrial robots, or enabled by powerful servers in the cloud, we can help you achieve your vision. We enable the smartness in your products with our PowerVR Neural Network Accelerators (NNA) and GPUs. Our NC-SDK enables seamless deployment of AI acceleration on either our hardware IP either in isolation or combined. Our NNA provides maximum efficiency with a scalable architecture which enables a wide range of smart edge and end point devices from low performance IoT to high performance RoboTaxi.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="CEVA"></a></p>
 
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/CEVA_logo.png" height="40"></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/CEVA_logo.png" height="40" loading="lazy" alt="CEVA" /></div>
 
-<div align="center"><h3> </h3></div>
 <p><a href="https://www.ceva-dsp.com/app/deep-learning/">Deep learning for the real-time embedded world</a></p>
 <blockquote>
   <p>One solution lies in supplying a dedicated low power AI processor for Deep Learning at the edge, combined with a deep neural network (DNN) graph compiler</p>
 </blockquote>
 
-
-<div align="center"><h3> </h3></div>
 <p><a name="Cadence"></a></p>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cadence_logo.png" height="40"></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cadence_logo.png" height="40" loading="lazy" alt="Cadence" /></div>
 
-<div align="center"><h3> </h3></div>
 <p><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Tensilica AI Platform</a></p>
 
-<div align="center"><h3> </h3></div>
 <p><a name="VeriSilicon"></a></p>
-<div align="center"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/VeriSilicon_logo.png" height="40"></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/VeriSilicon_logo.png" height="40" loading="lazy" alt="VeriSilicon" /></div>
 
-<div align="center"><h3> </h3></div>
 <p><a href="https://www.verisilicon.com/en/IPPortfolio/VivanteNPUIP">Vivante® NPU IP</a></p>
 <blockquote>
   <p>VeriSilicon's Neural Network Processor (NPU) IP is a highly scalable, programmable computer vision and artificial intelligence processor that supports AI operations upgrades for endpoints, edge devices, and cloud devices. Designed to meet a variety of chip sizes and power budgets, the Vivante NPU IP is a cost-effective, high-quality neural network acceleration engine solution.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
-<div align="center"><h2><a name="Startups"></a>IV. Startups</h2></div>
-
-<p><HR></p>
+<div align="center" class="section-title"><h2><a name="Startups"></a>IV. Startups</h2></div>
 
-<div align="center"><h3> </h3></div>
+<hr class="section-rule" />
 
 <p><a name="Cerebras"></a></p>
 
-<div align="center"><a href="https://www.cerebras.net/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cerebras_logo.png" height="50"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.cerebras.net/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cerebras_logo.png" height="50" loading="lazy" alt="Cerebras" /></a></div>
 
 <p><strong><a href="https://www.cerebras.net/press-release/cerebras-announces-third-generation-wafer-scale-engine">Cerebras Systems Unveils World’s Fastest AI Chip with 4 Trillion Transistors (WSE-3)</a></strong></p>
 <blockquote>
@@ -662,11 +572,9 @@
   <p>Today, the company announced the launch of its end-user compute product, the Cerebras CS-1, and also announced its first customer of Argonne National Laboratory.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Graphcore"></a></p>
 
-<div align="center"><a href="https://www.graphcore.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Graphcore_logo.png" height="70"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.graphcore.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Graphcore_logo.png" height="70" loading="lazy" alt="Graphcore" /></a></div>
 
 <p><strong><a href="https://www.graphcore.ai/posts/graphcore-joins-softbank-group-to-build-next-generation-of-ai-compute">Graphcore joins SoftBank Group to build next generation of AI compute</a></strong></p>
 <blockquote>
@@ -687,20 +595,13 @@
   <p>Graphcore, the Bristol-based startup that designs processors specifically for artificial intelligence applications, announced it has raised another $150 million in funding for R&D and to continue bringing on new customers. It’s valuation is now $1.95 billion.</p>
 </blockquote>
 
-
-<div align="center"><h3> </h3></div>
-
 <p><a href="https://mp.weixin.qq.com/s/CH9h8dUtoNK_2ZfkK5YU0g">解密又一个xPU：Graphcore的IPU</a> give some analysis on its IPU architecture.</p>
 <p><a href="https://mp.weixin.qq.com/s/AMuqeaShqEv3DnibH3scEA">Graphcore AI芯片：更多分析</a> More analysis.</p>
 <p><a href="https://mp.weixin.qq.com/s/qP0zsSA7SQWXDqWGEAXmOg">深度剖析AI芯片初创公司Graphcore的IPU</a> In-depth analysis after more information was disclosed.</p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Tenstorrent"></a></p>
 
-<div align="center"><a href="http://tenstorrent.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tenstorrent_logo.png" height="100"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://tenstorrent.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tenstorrent_logo.png" height="100" loading="lazy" alt="Tenstorrent" /></a></div>
 
 <p><strong><a href="https://tenstorrent.com/">Tenstorrent Blackhole / Wormhole AI processors</a></strong></p>
 <blockquote>
@@ -714,90 +615,68 @@
  
 <p><a href="https://www.anandtech.com/show/16709/an-interview-with-tenstorrent-ceo-ljubisa-bajic-and-cto-jim-keller">An Interview with Tenstorrent: CEO Ljubisa Bajic and CTO Jim Keller</a></p>
  
-<div align="center"><h3> </h3></div>
 
 <p><a name="Blaize"></a></p>
 
-<div align="center"><a href="https://www.blaize.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blaize_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.blaize.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blaize_logo.png" height="40" loading="lazy" alt="Blaize" /></a></div>
 
 <p><a href="https://www.eetimes.com/automotive-ai-startup-blaize-closes-71-million-funding-round/">Automotive AI Startup Blaize Closes $71 Million Funding Round</a></p>
 <blockquote>
   <p>Blaize, formerly ThinCI, has closed a Series D round of funding at $71 million. New investor Franklin Templeton and existing investor Temasek led the round, along with participation from Denso and other new and existing investors. This round brings Blaize’s total funding to around $155 million total.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Koniku"></a></p>
 
-<div align="center"><a href="http://koniku.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Koniku_logo.png" height="50"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://koniku.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Koniku_logo.png" height="50" loading="lazy" alt="Koniku" /></a></div>
 
 <blockquote>
   <p>Founded in 2014, Newark, California startup <a href="http://koniku.io/">Koniku</a> has taken in $1.65 million in funding so far to become “the world’s first neurocomputation company“. The idea is that since the brain is the most powerful computer ever devised, why not reverse engineer it? Simple, right? Koniku is actually integrating biological neurons onto chips and has made enough progress that they claim to have AstraZeneca as a customer. Boeing has also signed on with a letter of intent to use the technology in chemical-detecting drones.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Adapteva"></a></p>
 
-<div align="center"><a href="http://www.adapteva.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Adapteva_logo.png" height="70"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://www.adapteva.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Adapteva_logo.png" height="70" loading="lazy" alt="Adapteva" /></a></div>
 
 <p><a href="http://www.adapteva.com/">Adapteva</a> has taken in $5.1 million in funding from investors that include mobile giant Ericsson. <a href="http://www.parallella.org/docs/e5_1024core_soc.pdf">The paper "Epiphany-V: A 1024 processor 64-bit RISC System-On-Chip"</a> describes the design of Adapteva's 1024-core processor chip in 16nm FinFet technology. </p>
 
 <p><a name="Mythic"></a></p>
 
-<div align="center"><a href="https://mythic.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Mythic_logo.png" height="20"></a></div>
+<div align="center" class="vendor-logo"><a href="https://mythic.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Mythic_logo.png" height="20" loading="lazy" alt="Mythic" /></a></div>
 
-<div align="center"><h3> </h3></div>
-
-<p><a href="https://www.linkedin.com/pulse/era-analog-compute-has-arrived-michael-b-henry/">The Era of Analog Compute has Arrived!</a></p>
+<p><strong><a href="https://www.businesswire.com/news/home/20251217546249/en/Mythic-to-Challenge-AIs-GPU-Pantheon-with-100x-Energy-Advantage-and-Oversubscribed-%24125M-Raise">Mythic raises $125M Series D for analog AI compute</a></strong></p>
 <blockquote>
-  <p>ResNet-50 in our prototype analog AI processor. Production release will support 900-1000 fps and INT8 accuracy at 3W.</p>
+  <p>Dec 2025 — Oversubscribed round led by DCVC (strategic participation including Honda and Lockheed Martin) to scale Mythic’s Analog Processing Units (APUs) — flash-based compute-in-memory for energy-efficient inference at the edge and in data centers. Roadmap emphasizes chiplet-based next-gen APUs for robotics, automotive, and defense.</p>
 </blockquote>
- 
-<p><a href="https://venturebeat.com/2021/06/07/mythic-launches-analog-ai-processor-that-consumes-10-times-less-power/">Mythic launches analog AI processor that consumes 10 times less power</a></p>
+
+<p><strong><a href="https://mythic.ai/whats-new/mythic-acquires-videantis-one-of-europes-leading-digital-processor-ip-companies-to-build-the-worlds-most-energy-efficient-ai-compute-platform/">Mythic acquires Videantis for hybrid analog/digital AI platform</a></strong></p>
 <blockquote>
-  <p>Analog AI processor company Mythic launched its M1076 Analog Matrix Processor today to provide low-power AI processing.</p>
+  <p>May 2026 — Acquisition of Videantis GmbH adds a production digital processor IP + software stack alongside Mythic’s analog in-memory compute, targeting a hybrid platform with large claimed efficiency gains vs. conventional GPU inference.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
+<p><a href="https://mythic.ai/products/m1076-analog-matrix-processor/">M1076 Analog Matrix Processor</a> (shipping product context) ·
+<a href="https://venturebeat.com/2021/06/07/mythic-launches-analog-ai-processor-that-consumes-10-times-less-power/">VentureBeat: M1076 launch</a> (historical)</p>
 
 <p><a name="Brainchip"></a></p>
 
-<div align="center"><a href="http://www.brainchipinc.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Brainchip_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://www.brainchipinc.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Brainchip_logo.png" height="40" loading="lazy" alt="BrainChip" /></a></div>
 
-<p><a href="hhttps://venturebeat.com/2022/01/18/brainchip-launches-neuromorphic-process-for-ai-at-the-edge/">BrainChip launches neuromorphic process for AI at the edge</a> </p>
+<p><a href="https://venturebeat.com/2022/01/18/brainchip-launches-neuromorphic-process-for-ai-at-the-edge/">BrainChip launches neuromorphic process for AI at the edge</a> </p>
 <blockquote>
   <p>BrainChip today announced the commercialization of its Akida neural networking processor. Aimed at a variety of edge and internet of things (IoT) applications, BrainChip claims to be the first commercial producer of neuromorphic AI chips, which could deliver benefits in ultra-low power and performance over conventional approaches.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Deepvision"></a></p>
 
-<div align="center"><a href="https://deepvision.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Deepvision_logo.png" height="40"></a></div>
+<div align="center" class="vendor-logo"><a href="https://deepvision.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Deepvision_logo.png" height="40" loading="lazy" alt="Deep Vision" /></a></div>
 
-<div align="center"><h3> </h3></div>
-
-<p><a href="AI Processor Chipmaker Deep Vision Raises $35 Million in Series B Funding">AI Processor Chipmaker Deep Vision Raises $35 Million in Series B Funding</a></p>
+<p><a href="https://www.businesswire.com/news/home/20210928005270/en/AI-Processor-Chipmaker-Deep-Vision-Raises-35-Million-in-Series-B-Funding">AI Processor Chipmaker Deep Vision Raises $35 Million in Series B Funding</a></p>
 <blockquote>
   <p>Tiger Global Leads Series B Financing, Enabling Deep Vision to Expand Video Analytics and Natural Language Processing Capabilities in Edge Computing Applications</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Groq"></a></p>
 
-<div align="center"><h2><a href="http://groq.com/">Groq</a></h2></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-title"><h3><a href="https://groq.com/">Groq</a></h3></div>
 
 <p><strong><a href="https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Vera-Rubin-Opens-Agentic-AI-Frontier/default.aspx">NVIDIA Vera Rubin Opens Agentic AI Frontier (includes Groq LPU)</a></strong></p>
 <blockquote>
@@ -814,23 +693,15 @@
   <p>Jonathan Ross left Google to launch next-generation semiconductor startup Groq in 2016. Today, the Mountain View, California-based firm said that it had raised $300 million led by Tiger Global Management and billionaire investor Dan Sundheim’s D1 Capital as it officially launched into public view. </p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Kneron"></a></p>
 
-<div align="center"><a href="http://www.kneron.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Kneron_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://www.kneron.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Kneron_logo.png" height="60" loading="lazy" alt="Kneron" /></a></div>
 
 <p><a href="https://www.prnewswire.com/news-releases/kneron-to-accelerate-edge-ai-development-with-more-than-10-million-usd-series-a-financing-300556674.html">Kneron to Accelerate Edge AI Development with more than 10 Million USD Series A Financing</a></p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="GTI"></a></p>
 
-<div align="center"><a href="https://www.gyrfalcontech.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GTI_Logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.gyrfalcontech.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GTI_Logo.png" height="40" loading="lazy" alt="Gyrfalcon" /></a></div>
 
 <p>According to this article, <a href="https://www.prnewswire.com/news-releases/gyrfalcon-offers-automotive-ai-chip-technology-300860069.html">"Gyrfalcon offers Automotive AI Chip Technology"</a></p>
 
@@ -838,65 +709,46 @@
   <p>Gyrfalcon Technology Inc. (GTI), has been promoting matrix-based application specific chips for all forms of AI since offering their production versions of AI accelerator chips in September 2017. Through the licensing of its proprietary technology, the company is confident it can help automakers bring highly competitive AI chips to production for use in vehicles within 18 months, along with significant gains in AI performance, improvements in power dissipation and cost advantages.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="SambaNova"></a></p>
 
-<div align="center"><a href="https://sambanovasystems.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SambaNova_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://sambanova.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SambaNova_logo.png" height="40" loading="lazy" alt="SambaNova" /></a></div>
 
-<p><strong><a href="https://venturebeat.com/ai/sambanova-unveils-new-ai-chip-to-power-full-stack-ai-platform/">SambaNova unveils new AI chip to power full-stack AI platform</a></strong></p>
+<p><strong><a href="https://sambanova.ai/press/sambanova-unveils-fastest-chip-for-agentic-ai-collaborates-with-intel-and-raises-350m">SambaNova unveils SN50 RDU for agentic AI, Intel collaboration, $350M+ Series E</a></strong></p>
 <blockquote>
-  <p>Today Palo-Alto-based SambaNova Systems unveiled a new AI chip, the SN40L, which will power its full-stack large language model (LLM) platform, the SambaNova Suite, that helps enterprises go from chip to model — building and deploying customized generative AI models.</p>
+  <p>Feb 2026 — Fifth-generation Reconfigurable Dataflow Unit (SN50) targets large-scale agentic inference (vendor claims up to ~5× compute and ~4× network bandwidth vs. SN40L; scales toward 256 accelerators). SoftBank named as an early deploy partner; shipping targeted for H2 2026. Planned multi-year collaboration with Intel for heterogeneous inference stacks; oversubscribed Series E led with Vista / Cambium participation including Intel Capital.</p>
 </blockquote>
 
-<p><a href="https://techcrunch.com/2021/04/13/sambanova-raises-676m-at-a-5-1b-valuation-to-double-down-on-cloud-based-ai-software-for-enterprises/">SambaNova raises $676M at a $5.1B valuation to double down on cloud-based AI software for enterprises</a></p>
-<blockquote>
-  <p>SambaNova — a startup building AI hardware and integrated systems that run on it that only officially came out of three years in stealth last December — is announcing a huge round of funding today to take its business out into the world. The company has closed on $676 million in financing, a Series D that co-founder and CEO Rodrigo Liang has confirmed values the company at $5.1 billion.</p>
-</blockquote>
+<p><a href="https://sambanova.ai/blog/introducing-the-sn50-rdu-purpose-built-for-agentic-inference">Introducing the SN50 RDU</a> ·
+<a href="https://sambanova.ai/products/rdu-ai-chips">RDU product page</a></p>
 
-<p><a href="https://sambanova.ai/articles/introducing-sambanova-systems-datascale-a-new-era-of-computing/">Introducing SambaNova Systems DataScale: A New Era of Computing</a></p>
+<p><strong><a href="https://venturebeat.com/ai/sambanova-unveils-new-ai-chip-to-power-full-stack-ai-platform/">SN40L RDU powers SambaNova Suite</a></strong> (prior gen)</p>
 <blockquote>
-  <p>SambaNova has been working closely with many organizations the past few months and has established a new state of the art in NLP. This advancement in NLP deep learning is illustrated by a GPU-crushing, world record performance result achieved on SambaNova Systems’ Dataflow-optimized system. </p>
+  <p>Fourth-generation SN40L RDU with three-tier memory (SRAM / HBM / DRAM) for full-stack LLM platforms — still the primary shipping generation ahead of SN50.</p>
 </blockquote>
 
-<p><a href="https://sambanova.ai/a-new-state-of-the-art-in-nlp-beyond-gpus/">A New State of the Art in NLP: Beyond GPUs</a></p>
-<blockquote>
-  <p>SambaNova has been working closely with many organizations the past few months and has established a new state of the art in NLP. This advancement in NLP deep learning is illustrated by a GPU-crushing, world record performance result achieved on SambaNova Systems’ Dataflow-optimized system. </p>
-</blockquote>
+<p><a href="https://techcrunch.com/2021/04/13/sambanova-raises-676m-at-a-5-1b-valuation-to-double-down-on-cloud-based-ai-software-for-enterprises/">Series D funding context</a> (historical) ·
+<a href="https://sambanova.ai/articles/introducing-sambanova-systems-datascale-a-new-era-of-computing/">DataScale systems</a></p>
 
-
-<div align="center"><h3> </h3></div>
 <p><a name="GreenWaves"></a></p>
-<div align="center"><a href="https://greenwaves-technologies.com/en/greenwaves-technologies-2/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GreenWaves_logo.png" height="50"></a></div>
+<div align="center" class="vendor-logo"><a href="https://greenwaves-technologies.com/en/greenwaves-technologies-2/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GreenWaves_logo.png" height="50" loading="lazy" alt="GreenWaves" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><a href="https://www.eetimes.eu/greenwaves-shows-off-advanced-audio-demos/">GreenWaves Shows Off Advanced Audio Demos</a></p>
 <blockquote>
   <p>The Gap9 processor, a successor to Gap8 which targets computer vision in IoT devices, is an ultra-low power neural network processor suitable for battery-powered devices. GreenWaves’ vice president of marketing Martin Croome told EE Times Europe that the company decided to focus Gap9 on the hearables market after receiving traction from this sector for Gap8.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Lightelligence"></a></p>
 
-<div align="center"><a href="https://www.lightelligence.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightelligence_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.lightelligence.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightelligence_logo.png" height="60" loading="lazy" alt="Lightelligence" /></a></div>
 
 <p><strong><a href="https://www.eetimes.com/optical-computing-chip-runs-hardest-math-problems-100x-faster-than-gpus/">Optical Chip Solves Hardest Math Problems Faster than GPUs</a></strong></p>
 <blockquote>
   <p>Optical computing startup Lightelligence has demonstrated a silicon photonics accelerator running the Ising problem more than 100 times faster than a typical GPU setup.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Lightmatter"></a></p>
 
-<div align="center"><a href="https://www.lightmatter.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightmatter_logo.png" height="50"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.lightmatter.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightmatter_logo.png" height="50" loading="lazy" alt="Lightmatter" /></a></div>
 
 <p><a href="https://www.eetimes.com/lightmatter-raises-more-funding-for-photonic-ai-chip/">Lightmatter Raises More Funding for Photonic AI Chip</a></p>
 <blockquote>
@@ -905,48 +757,42 @@
 
 <p><a name="Hailo"></a></p>
 
-<div align="center"><a href="https://www.hailotech.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Hailo_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://hailo.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Hailo_logo.png" height="60" loading="lazy" alt="Hailo" /></a></div>
 
-<div align="center"><h3> </h3></div>
+<p><strong><a href="https://hailo.ai/company-overview/newsroom/news/hailo-announces-general-availability-of-hailo-10h-edge-ai-accelerator-with-generative-ai-capabilities/">Hailo-10H generally available for edge generative AI</a></strong></p>
+<blockquote>
+  <p>Jul 2025 — Second-generation Hailo-10H brings on-device LLM / VLM / GenAI capabilities to the edge (~40 TOPS INT4 class, ~2.5W typical), while retaining vision workloads from the Hailo-8 generation. Automotive-qualified (AEC-Q100 Grade 2) with 2026 SOP targeting for some designs.</p>
+</blockquote>
 
-<p><a href="https://www.eetimes.com/unicorn-ai-chipmaker-hailo-raises-136-million/">‘Unicorn’ AI Chipmaker Hailo Raises $136 Million</a></p>
+<p><a href="https://hailo.ai/products/ai-accelerators/hailo-10h-ai-accelerator/">Hailo-10H product page</a> ·
+<a href="https://hailo.ai/products/ai-accelerators/hailo-8-ai-accelerator/">Hailo-8</a></p>
+
+<p><a href="https://www.eetimes.com/unicorn-ai-chipmaker-hailo-raises-136-million/">‘Unicorn’ AI Chipmaker Hailo Raises $136 Million</a> (historical)</p>
 <blockquote>
   <p>Israeli AI chip startup Hailo has raised $136 million in a Series C funding round, bringing the company’s total to $224 million. The company has also reportedly reached “unicorn” status.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Tachyum"></a></p>
 
-<div align="center"><a href="http://www.tachyum.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tachyum_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://www.tachyum.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tachyum_logo.png" height="40" loading="lazy" alt="Tachyum" /></a></div>
 
 <p><a href="https://www.hpcwire.com/off-the-wire/tachyum-launches-prodigy-universal-processor/">Tachyum Launches Prodigy Universal Processor</a></p>
 <blockquote>
   <p>May 11, 2021 — Tachyum today launched the world’s first universal processor, Prodigy, which unifies the functionality of a CPU, GPU and TPU in a single processor, creating a homogeneous architecture, while delivering massive performance improvements at a cost many times less than competing products</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Alphaics"></a></p>
 
-<div align="center"><a href="https://www.alphaics.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Alphaics_logo.png" height="50"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.alphaics.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Alphaics_logo.png" height="50" loading="lazy" alt="AlphaICs" /></a></div>
 
 <p><a href="https://www.eetimes.com/alphaics-begins-sampling-its-deep-learning-co-processor/">AlphaICs Begins Sampling Its Deep Learning Co-Processor</a></p>
 <blockquote>
   <p>AlphaICs, a startup developing edge AI and learning silicon aimed at smart vision applications, is sampling its deep learning co-processor, Gluon, that also comes with a software development kit.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Syntiant"></a></p>
 
-<div align="center"><a href="https://www.syntiant.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Syntiant_logo.png" height="30"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.syntiant.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Syntiant_logo.png" height="30" loading="lazy" alt="Syntiant" /></a></div>
 
 <p><a href="https://semiengineering.com/syntiant-analog-deep-learning-chips/">Syntiant: Analog Deep Learning Chips</a></p>
 
@@ -954,13 +800,9 @@
   <p>Startup Syntiant Corp. is an Irvine, Calif. semiconductor company led by former top Broadcom engineers with experience in both innovative design and in producing chips designed to be produced in the billions, according to company CEO Kurt Busch.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="aiCTX"></a></p>
 
-<div align="center"><a href="https://aictx.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/aiCTX_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://aictx.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/aiCTX_logo.png" height="40" loading="lazy" alt="aiCTX" /></a></div>
 
 <p><strong><a href="https://www.eetimes.com/document.asp?doc_id=1333983">Baidu Backs Neuromorphic IC Developer</a></strong></p>
 
@@ -970,9 +812,7 @@
 
 <p><a name="Flexlogix"></a></p>
 
-<div align="center"><a href="http://www.flex-logix.com/nmax"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/flexlogix_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://www.flex-logix.com/nmax"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/flexlogix_logo.png" height="40" loading="lazy" alt="Flex Logix" /></a></div>
 
 <p><strong><a href="https://www.zdnet.com/article/flex-logix-has-two-paths-to-making-a-lot-of-money-challenging-nvidia-in-ai/">Flex Logix has two paths to making a lot of money challenging Nvidia in AI</a></strong></p>
 
@@ -982,9 +822,7 @@
 
 <p><a name="PFN"></a></p>
 
-<div align="center"><a href="https://projects.preferred.jp/mn-core/en/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/PFN_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://projects.preferred.jp/mn-core/en/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/PFN_logo.png" height="40" loading="lazy" alt="Preferred Networks" /></a></div>
 
 <p><strong><a href="https://www.preferred-networks.jp/en/news">Preferred Networks develops a custom deep learning processor MN-Core for use in MN-3, a new large-scale cluster, in spring 2020</a></strong></p>
 
@@ -995,9 +833,7 @@
 
 <p><a name="Cornami"></a></p>
 
-<div align="center"><a href="http://cornami.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cornami_logo.jpg" height="30"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://cornami.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cornami_logo.jpg" height="30" loading="lazy" alt="Cornami" /></a></div>
 
 <p><strong><a href="https://www.zdnet.com/article/ai-startup-cornami-reveals-details-of-neural-net-chip/">AI Startup Cornami reveals details of neural net chip</a></strong></p>
 
@@ -1008,9 +844,7 @@
 
 <p><a name="Anaflash"></a></p>
 
-<div align="center"><a href="http://anaflash.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Anaflash_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://anaflash.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Anaflash_logo.png" height="40" loading="lazy" alt="Anaflash" /></a></div>
 
 <p><strong><a href="https://www.smart2zero.com/news/ai-chip-startup-offers-new-edge-computing-solution">AI chip startup offers new edge computing solution</a></strong></p>
 
@@ -1021,9 +855,7 @@
 
 <p><a name="Optalysys"></a></p>
 
-<div align="center"><a href="https://www.optalysys.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Optalysys_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.optalysys.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Optalysys_logo.png" height="40" loading="lazy" alt="Optalysys" /></a></div>
 
 <p><strong><a href="https://www.globenewswire.com/news-release/2019/03/07/1749510/0/en/Optalysys-launches-world-s-first-commercial-optical-processing-system-the-FT-X-2000.html">Optalysys launches world’s first commercial optical processing system, the FT:X 2000</a></strong></p>
 
@@ -1034,9 +866,7 @@
 
 <p><a name="etacompute"></a></p>
 
-<div align="center"><a href="https://etacompute.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/etacompute_logo.png" height="80"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://etacompute.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/etacompute_logo.png" height="80" loading="lazy" alt="Eta Compute" /></a></div>
 
 <p><a href="https://spectrum.ieee.org/tech-talk/semiconductors/processors/lowpower-ai-startup-eta-compute-delivers-first-commercial-chips">Low-Power AI Startup Eta Compute Delivers First Commercial Chips</a></p>
 <blockquote>
@@ -1050,9 +880,7 @@
 
 <p><a name="Achronix"></a></p>
 
-<div align="center"><a href="https://www.achronix.com/product/speedster7t/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Achronix_logo.png" height="30"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.achronix.com/product/speedster7t/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Achronix_logo.png" height="30" loading="lazy" alt="Achronix" /></a></div>
 
 <p><strong><a href="https://www.eetimes.com/document.asp?doc_id=1334717">Achronix Rolls 7-nm FPGAs for AI</a></strong></p>
 
@@ -1062,9 +890,7 @@
 
 <p><a name="Areanna"></a></p>
 
-<div align="center"><a href="https://areanna-ai.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Areanna_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://areanna-ai.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Areanna_logo.png" height="60" loading="lazy" alt="Areanna" /></a></div>
 
 <p><strong><a href="https://www.eetimes.com/document.asp?doc_id=1334947#">Startup Runs AI in Novel SRAM</a></strong></p>
 
@@ -1074,9 +900,7 @@
 
 <p><a name="Neuroblade"></a></p>
 
-<div align="center"><a href="https://www.neuroblade.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neuroblade_logo.png" height="120"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.neuroblade.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neuroblade_logo.png" height="120" loading="lazy" alt="NeuroBlade" /></a></div>
 
 <p><strong><a href="https://www.eetasia.com/news/article/NeuroBlade-Preps-Inference-Chip">NeuroBlade Preps Inference Chip</a></strong></p>
 
@@ -1086,9 +910,7 @@
 
 <p><a name="Luminous"></a></p>
 
-<div align="center"><a href="https://www.luminouscomputing.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Luminous_logo.png" height="90"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.luminouscomputing.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Luminous_logo.png" height="90" loading="lazy" alt="Luminous" /></a></div>
 
 <p><strong><a href="https://www.technologyreview.com/s/613668/ai-chips-uses-optical-semiconductor-machine-learning/">Bill Gates just backed a chip startup that uses light to turbocharge AI</a></strong></p>
 
@@ -1098,9 +920,7 @@
 
 <p><a name="Efinix"></a></p>
 
-<div align="center"><a href="https://www.efinixinc.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Efinix_logo.png" height="25"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.efinixinc.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Efinix_logo.png" height="25" loading="lazy" alt="Efinix" /></a></div>
 
 <p><strong><a href="https://www.zdnet.com/article/chip-startup-efinix-hopes-to-bootstrap-ai-efforts-in-iot/">Chip startup Efinix hopes to bootstrap AI efforts in IoT</a></strong></p>
 
@@ -1110,9 +930,7 @@
 
 <p><a name="AIstorm"></a></p>
 
-<div align="center"><a href="https://aistorm.ai"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AIstorm_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://aistorm.ai"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AIstorm_logo.png" height="60" loading="lazy" alt="AIstorm" /></a></div>
 
 <p><strong><a href="https://venturebeat.com/2019/02/11/aistorm-raises-13-2-million-for-ai-edge-computing-chips/">AIStorm raises $13.2 million for AI edge computing chips</a></strong></p>
 
@@ -1120,12 +938,9 @@
   <p>David Schie, a former senior executive at Maxim, Micrel, and Semtech, thinks both markets are ripe for disruption. He — along with WSI, Toshiba, and Arm veterans Robert Barker, Andreas Sibrai, and Cesar Matias — in 2011 cofounded AIStorm, a San Jose-based artificial intelligence (AI) startup that develops chipsets that can directly process data from wearables, handsets, automotive devices, smart speakers, and other internet of things (IoT) devices. </p>
 </blockquote>
 
-
 <p><a name="SiMa"></a></p>
 
-<div align="center"><a href="https://sima.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SiMa_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://sima.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SiMa_logo.png" height="40" loading="lazy" alt="SiMa.ai" /></a></div>
 
 <p><strong><a href="https://sima.ai/press-release/sima-ai-next-gen-platform-for-physical-ai-in-production/">SiMa.ai Modalix platform for Physical AI in production</a></strong></p>
 <blockquote>
@@ -1147,21 +962,16 @@
 
 <p><a name="Untether"></a></p>
 
-<div align="center"><a href="https://untether.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Untether_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://untether.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Untether_logo.png" height="40" loading="lazy" alt="Untether AI" /></a></div>
 
 <p><strong><a href="https://venturebeat.com/2021/07/20/untether-ai-nabs-125m-for-ai-acceleration-chips/">Untether AI nabs $125M for AI acceleration chips</a></strong></p>
 <blockquote>
   <p>Untether AI, a startup developing custom-built chips for AI inferencing workloads, today announced it has raised $125 million from Tracker Capital Management and Intel Capital. The round, which was oversubscribed and included participation from Canada Pension Plan Investment Board and Radical Ventures, will be used to support customer expansion.</p>
 </blockquote>
 
-
 <p><a name="GrAI"></a></p>
 
-<div align="center"><a href="https://www.graimatterlabs.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GrAI_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.graimatterlabs.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GrAI_logo.png" height="40" loading="lazy" alt="GrAI Matter Labs" /></a></div>
 
 <p><strong><a href="https://venturebeat.com/2019/09/18/grai-matter-labs-reveals-neuronflow-technology-and-announces-graiflow-sdk/">GrAI Matter Labs Reveals NeuronFlow Technology and Announces GrAIFlow SDK</a></strong></p>
 
@@ -1171,9 +981,7 @@
 
 <p><a name="Rain"></a></p>
 
-<div align="center"><a href="http://rain-neuromorphics.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Rain_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://rain-neuromorphics.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Rain_logo.png" height="40" loading="lazy" alt="Rain" /></a></div>
 
 <p><strong><a href="https://www.crunchbase.com/organization/rain-neuromorphics">Rain Neuromorphics on Crunchbase</a></strong></p>
 <blockquote>
@@ -1182,9 +990,7 @@
 
 <p><a name="ABR"></a></p>
 
-<div align="center"><a href="https://appliedbrainresearch.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ABR_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://appliedbrainresearch.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ABR_logo.png" height="40" loading="lazy" alt="Applied Brain Research" /></a></div>
 
 <p><strong><a href="https://www.crunchbase.com/organization/applied-brain-research">Applied Brain Research on Crunchbase</a></strong></p>
 <blockquote>
@@ -1193,9 +999,7 @@
 
 <p><a name="Xmos"></a></p>
 
-<div align="center"><a href="https://www.xmos.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Xmos_logo.png" height="40"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.xmos.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Xmos_logo.png" height="40" loading="lazy" alt="XMOS" /></a></div>
 
 <p><strong><a href="https://www.eetimes.com/xmos-adapts-xcore-into-aiot-crossover-processor/">XMOS adapts Xcore into AIoT ‘crossover processor’</a></strong></p>
 <blockquote>
@@ -1209,9 +1013,7 @@
 
 <p><a name="DinoplusAI"></a></p>
 
-<div align="center"><a href="http://dinoplus.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/DinoplusAI_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://dinoplus.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/DinoplusAI_logo.png" height="60" loading="lazy" alt="DinoplusAI" /></a></div>
 
 <blockquote>
   <p>We design and produce AI processors and the software to run them in data centers. Our unique approach optimizes for inference with the focus on performance, power efficiency, and ease of use; and at the same time our approach enables cost-effective training. </p>
@@ -1219,19 +1021,23 @@
 
 <p><a name="Furiosa"></a></p>
 
-<div align="center"><a href="https://www.furiosa.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Furiosa_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.furiosa.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Furiosa_logo.png" height="60" loading="lazy" alt="FuriosaAI" /></a></div>
 
-<div align="center"><h3> </h3></div>
+<p><strong><a href="https://furiosa.ai/blog/rngd-enters-mass-production-the-high-performance-ai-accelerator-for-any-data-center">RNGD enters mass production for data-center AI inference</a></strong></p>
+<blockquote>
+  <p>Jan 2026 — FuriosaAI’s RNGD (Tensor Contraction Processor, TSMC 5nm, ~180W PCIe) entered volume shipment for LLM / agentic inference; NXT RNGD servers pack multiple cards into air-cooled, ~3kW-class systems.</p>
+</blockquote>
 
+<p><strong><a href="https://furiosa.ai/blog/furiosaai-partners-with-broadcom-to-build-next-generation-inference-platform-for-the-agentic-era">FuriosaAI partners with Broadcom on next-generation inference platform</a></strong></p>
 <blockquote>
-  <p>We build high-performance AI inference coprocessors that can be seamlessly integrated into various computing platforms including data centers, servers, desktops, automobiles and robots. </p>
+  <p>May 2026 — Strategic collaboration to evolve TCP into a scale-up multi-die inference platform with Broadcom Ethernet/PCIe switching for hyperscale agentic workloads.</p>
 </blockquote>
 
-<p><a name="Corerain"></a></p>
+<p><a href="https://furiosa.ai/blog/furiosaai-equinixs-lisbon-data-center-press-release">Equinix Lisbon RNGD deployment</a> (Jul 2026)</p>
 
-<div align="center"><a href="http://www.corerain.com/en"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Corerain_logo.png" height="60"></a></div>
+<p><a name="Corerain"></a></p>
 
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="http://www.corerain.com/en"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Corerain_logo.png" height="60" loading="lazy" alt="Corerain" /></a></div>
 
 <blockquote>
   <p>Corerain provides ultra-high performance AI acceleration chips and the world's first streaming engine-based AI development platform.</p>
@@ -1239,9 +1045,7 @@
 
 <p><a name="Perceive"></a></p>
 
-<div align="center"><a href="https://perceive.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Perceive_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://perceive.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Perceive_logo.png" height="60" loading="lazy" alt="Perceive" /></a></div>
 
 <p><a href="https://venturebeat.com/2020/03/31/perceive-emerges-from-stealth-with-ergo-edge-ai-chip/">Perceive emerges from stealth with Ergo edge AI chip</a></p>
 <blockquote>
@@ -1250,9 +1054,7 @@
 
 <p><a name="SimpleMachines"></a></p>
 
-<div align="center"><a href="https://www.simplemachines.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SimpleMachines_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.simplemachines.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SimpleMachines_logo.png" height="60" loading="lazy" alt="SimpleMachines" /></a></div>
 
 <p><a href="https://www.design-reuse.com/news/49012/simplemachines-ai-chip-tsmc-16nm.html">SimpleMachines, Inc. Debuts First-of-its-Kind High Performance Chip</a></p>
 <blockquote>
@@ -1263,9 +1065,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <p><a name="Neureality"></a></p>
 
-<div align="center"><a href="https://www.neureality.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neureality_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.neureality.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neureality_logo.png" height="60" loading="lazy" alt="NeuReality" /></a></div>
 
 <p><strong><a href="https://techcrunch.com/2022/12/06/neureality-ai-accelerator-chips-startup-raises-35m/">NeuReality lands $35M to bring AI accelerator chips to market</a></strong></p>
 <blockquote>
@@ -1283,72 +1083,63 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 </blockquote>
  
 <p><a name="AnalogInference"></a></p>
-<div align="center"><a href="https://www.analog-inference.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AnalogInference_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.analog-inference.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AnalogInference_logo.png" height="60" loading="lazy" alt="Analog Inference" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.eenewsanalog.com/news/analog-inference-startup-raises-106-million">Analog inference startup raises $10.6 million</a></strong></p>
 <blockquote>
   <p>The company is backed by Khosla Ventures and is developing its first generation of products for AI computing at the edge. The company raised $4.5 million shortly after its formation in March 2018, so the latest tranche brings the total raised to-date to $15.1 million</p>
 </blockquote>
 
 <p><a name="Quadric"></a></p>
-<div align="center"><a href="https://www.quadric.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Quatric_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.quadric.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Quatric_logo.png" height="60" loading="lazy" alt="Quadric" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.hpcwire.com/off-the-wire/quadric-announces-unified-silicon-and-software-platform-optimized-for-on-device-ai/">Quadric Announces Unified Silicon and Software Platform Optimized for On-Device AI</a></strong></p>
 <blockquote>
   <p>BURLINGAME, Calif., June 22, 2021 — Quadric (quadric.io), an innovator in high-performance edge processing, has introduced a unified silicon and software platform that unlocks the power of on-device AI. </p>
 </blockquote> 
 
 <p><a name="EdgeQ"></a></p>
-<div align="center"><a href="https://edgeq.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/EdgeQ_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://edgeq.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/EdgeQ_logo.png" height="60" loading="lazy" alt="EdgeQ" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><strong><a href="https://techcrunch.com/2021/01/26/edgeq-reveals-more-details-behind-its-next-gen-5g-ai-chip/">EdgeQ reveals more details behind its next-gen 5G/AI chip</a></strong></p>
 <blockquote>
   <p>5G is the current revolution in wireless technology, and every chip company old and new is trying to burrow their way into this ultra-competitive — but extremely lucrative — market. One of the most interesting new players in the space is EdgeQ, a startup with a strong technical pedigree via Qualcomm that we covered last year after it raised a nearly $40 million Series A.</p>
 </blockquote> 
  
 <p><a name="Innatera"></a></p>
-<div align="center"><a href="http://www.innatera.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Innatera_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="http://www.innatera.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Innatera_logo.png" height="60" loading="lazy" alt="Innatera" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.eetimes.com/innatera-unveils-neuromorphic-ai-chip-to-accelerate-spiking-networks/">Innatera Unveils Neuromorphic AI Chip to Accelerate Spiking Networks</a></strong></p>
 <blockquote>
   <p>Innatera, the Dutch startup making neuromorphic AI accelerators for spiking neural networks, has produced its first chips, gauged their performance, and revealed details of their architecture.</p>
 </blockquote> 
  
 <p><a name="Ceremorphic"></a></p>
-<div align="center"><a href="https://ceremorphic.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Ceremorphic_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://ceremorphic.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Ceremorphic_logo.png" height="60" loading="lazy" alt="Ceremorphic" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.eetimes.com/redpine-founder-launches-ai-processor-startup/">Redpine Founder Launches AI Processor Startup</a></strong></p>
 <blockquote>
   <p>Ceremorphic, an AI chip startup emerging from stealth mode this week, is readying a heterogeneous AI processor aimed at model training in data centers, automotive, high-performance computing, robotics and other emerging applications.</p>
 </blockquote> 
  
 <p><a name="Aspinity"></a></p>
-<div align="center"><a href="https://www.aspinity.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Aspinity_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.aspinity.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Aspinity_logo.png" height="60" loading="lazy" alt="Aspinity" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><strong><a href="https://embeddedcomputing.com/technology/analog-and-power/analog-semicundoctors-sensors/aspinity-analog-ml-chip-allows-battery-powered-always-on">Aspinity Analog ML Chip Allows Battery-Powered “Always On”</a></strong></p>
 <blockquote>
   <p>Machine learning (ML) is all about massive amounts of processing, DSP, etc., right? Maybe not, according to the team at Aspinity. The company continues to push ahead on the analog front. The latest member of the company’s analogML family, the AML100, operates completely in the analog domain. As a result, it can reduce always-on system power by 95% (for the record, we had to walk through this a couple of times before I believed them).</p>
 </blockquote> 
  
 <p><a name="Teramem"></a></p>
-<div align="center"><a href="https://www.tetramem.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Teramem_logo.png" height="60"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.tetramem.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Teramem_logo.png" height="60" loading="lazy" alt="TetraMem" /></a></div>
 
-<div align="center"><h3> </h3></div>
 <p><strong><a href="https://www.tetramem.com/posts/TetraMem-Technology-Debut-at-Linley">TetraMem enjoyed an exciting public debut of our analog in-memory compute technology at the Linley Spring 2022 Processor Conference.</a></strong></p>
 <blockquote>
   <p></p>
 </blockquote> 
  
 <p><a name="d-matrix"></a></p>
-<div align="center"><a href="https://www.d-matrix.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/d-matrix_logo.png" height="60"></a></div>
-
-<div align="center"><h3> </h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.d-matrix.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/d-matrix_logo.png" height="60" loading="lazy" alt="d-Matrix" /></a></div>
 
 <p><strong><a href="https://www.reuters.com/technology/ai-chip-startup-d-matrix-raises-110-mln-with-backing-microsoft-2023-09-06/">Exclusive: AI chip startup d-Matrix raises $110 million with backing from Microsoft</a></strong></p>
 <blockquote>
@@ -1360,8 +1151,6 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>The startup combines digital in-memory compute and chiplet implementations for data-center-grade inference.</p>
 </blockquote> 
  
-<div align="center"><h3> </h3></div>
-
 
 <p><a name="Etched"></a></p>
 <div align="center"><h3><a href="https://www.etched.com/">Etched</a></h3></div>
@@ -1376,19 +1165,14 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>2024 Series A coverage of Sohu: an ASIC purpose-built for Transformer inference (rather than general-purpose GPU programmability), aiming for higher tokens/sec and better energy efficiency on LLM serving.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
-
-<div align="center"><h2><a name="China_AI_Chips"></a>V. China AI Accelerator Vendors</h2></div>
-<p><HR></p>
+<div align="center" class="section-title"><h2><a name="China_AI_Chips"></a>V. China AI Accelerator Vendors</h2></div>
+<hr class="section-rule" />
 <p>Domestic training/inference accelerators have become central under export-control and procurement policies. Coverage below highlights widely reported product lines; treat performance claims as vendor-/press-reported unless independently benchmarked.</p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="HuaweiAscend"></a></p>
-<div align="center"><a href="https://www.huawei.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HUAWEI_logo.png" height="40"></a>
+<div align="center" class="vendor-logo"><a href="https://www.huawei.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HUAWEI_logo.png" height="40" loading="lazy" alt="HUAWEI logo" /></a>
 &nbsp;&nbsp;
-<a href="https://www.hisilicon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HiSilicon_logo.png" height="40"></a></div>
+<a href="https://www.hisilicon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HiSilicon_logo.png" height="40" loading="lazy" alt="HiSilicon logo" /></a></div>
 <div align="center"><h3>Huawei Ascend / HiSilicon</h3></div>
 
 <p><strong><a href="https://www.huawei.com/en/news/2025/9/hc-xu-keynote-speech">Huawei Atlas 900 A3 SuperPoD / Ascend 910C</a></strong></p>
@@ -1398,10 +1182,8 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <p><a href="https://arxiv.org/abs/2506.12708">Serving Large Language Models on Huawei CloudMatrix384</a> (technical paper)</p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Cambricon"></a></p>
-<div align="center"><a href="https://www.cambricon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cambricon_logo.png" height="50"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.cambricon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cambricon_logo.png" height="50" loading="lazy" alt="Cambricon" /></a></div>
 <div align="center"><h3>Cambricon (寒武纪)</h3></div>
 
 <p><strong><a href="https://www.tomshardware.com/tech-industry/semiconductors/cambricon-targets-500000-ai-chips-in-2026-as-china-accelerates-domestic-hardware-push">Cambricon targets 500,000 AI chips in 2026</a></strong></p>
@@ -1414,8 +1196,6 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>MLU500-series cloud accelerator commonly cited for China data-center training/inference deployments with MLU-Link multi-card interconnect and Cambricon’s NeuWare / BANG software stack.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Biren"></a></p>
 <div align="center"><h3><a href="https://www.birentech.com/">Biren Technology (壁仞)</a></h3></div>
 
@@ -1424,8 +1204,6 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Jan 2026 — Shanghai Biren Technology listed in Hong Kong (6082.HK), raising ~HK$5.58B. Best known for BR100-class GPGPU / AI accelerators positioned as high-performance domestic alternatives.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="MooreThreads"></a></p>
 <div align="center"><h3><a href="https://www.mthreads.com/">Moore Threads (摩尔线程)</a></h3></div>
 
@@ -1434,10 +1212,8 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Dec 2025 — Full-function GPU vendor (MTT S4000 / S5000 class AI cards, MUSA software stack) completed a ~$1.1B STAR Market IPO amid China’s domestic GPU push.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Enflame"></a></p>
-<div align="center"><a href="https://github.com/EnflameTechnology"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Enflame_logo.png" height="50"></a></div>
+<div align="center" class="vendor-logo"><a href="https://github.com/EnflameTechnology"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Enflame_logo.png" height="50" loading="lazy" alt="Enflame" /></a></div>
 <div align="center"><h3>Enflame (燧原)</h3></div>
 
 <p><strong><a href="https://www.hpcwire.com/off-the-wire/enflame-technology-announces-cloudblazer-with-dtu-chip-on-globalfoundries-12lp-finfet-platform-for-data-center-training/">Enflame CloudBlazer / DTU AI accelerators</a></strong></p>
@@ -1445,10 +1221,8 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Shanghai-based accelerator vendor offering CloudBlazer training/inference products (DTU architecture) for domestic intelligent-computing centers; frequently grouped with China’s “GPU dragons” alongside Biren, Moore Threads, and MetaX. Open-source ecosystem links: <a href="https://github.com/EnflameTechnology">EnflameTechnology on GitHub</a>.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Horizon"></a></p>
-<div align="center"><a href="https://www.horizon.auto/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Horizon_Robotics_logo.png" height="50"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.horizon.auto/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Horizon_Robotics_logo.png" height="50" loading="lazy" alt="Horizon Robotics" /></a></div>
 <div align="center"><h3>Horizon Robotics (地平线)</h3></div>
 
 <p><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Horizon Journey 6 / BPU Nash automotive AI processors</a></strong></p>
@@ -1456,11 +1230,8 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Leading China ADAS / autonomous-driving compute vendor with Journey-series BPU SoCs (Journey 6 adds BPU Nash with stronger Transformer support) widely designed into passenger vehicles; focuses on efficient vision/transformer perception at the edge rather than datacenter LLM training.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
-
 <p><a name="BlackSesame"></a></p>
-<div align="center"><a href="https://www.blacksesame.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blacksesame_logo.png" height="50"></a></div>
+<div align="center" class="vendor-logo"><a href="https://www.blacksesame.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blacksesame_logo.png" height="50" loading="lazy" alt="Black Sesame" /></a></div>
 <div align="center"><h3>Black Sesame Technologies (黑芝麻智能)</h3></div>
 
 <p><strong><a href="https://www.blacksesame.com/en/huashan-a1000/">Huashan A1000 automotive-grade ADAS / AD SoCs</a></strong></p>
@@ -1470,11 +1241,8 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <p><a href="https://www.caixinglobal.com/2024-08-01/chinese-auto-chip-maker-black-sesame-to-raise-143-million-in-hong-kong-ipo-102222110.html">Caixin: Black Sesame Hong Kong IPO</a></p>
 
-<div align="center"><h3> </h3></div>
-
-
 <p><a name="MetaX"></a></p>
-<div align="center"><h3><a href="https://www.metax-tech.com/en/about/about.html">MetaX (沐曦)</a></h3></div>
+<div align="center" class="vendor-title"><h3><a href="https://www.metax-tech.com/en/about/about.html">MetaX (沐曦)</a></h3></div>
 
 <p><strong><a href="https://www.caixinglobal.com/2026-03-26/chinese-gpu-maker-metax-doubles-revenue-amid-push-for-domestic-chips-102427800.html">Chinese GPU Maker MetaX Doubles Revenue Amid Push for Domestic Chips</a></strong></p>
 <blockquote>
@@ -1483,10 +1251,8 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <p><a href="https://www.chinadaily.com.cn/a/202607/09/WS6a4f3978a310986e2b4645f5.html">MetaX scales production to meet rising AI chip demand</a></p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Hygon"></a></p>
-<div align="center"><h3><a href="https://www.hygon.cn/">Hygon (海光) DCU</a></h3></div>
+<div align="center" class="vendor-title"><h3><a href="https://www.hygon.cn/">Hygon (海光) DCU</a></h3></div>
 
 <p><strong><a href="https://www.trendforce.com/news/2026/04/29/news-huawei-ascend-cambricon-and-hygon-completed-day-0-adaptation-to-deepseek-v4/">Hygon DCU completes Day-0 adaptation for DeepSeek-V4</a></strong></p>
 <blockquote>
@@ -1495,23 +1261,20 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <p><a href="https://github.com/HYGON-AI/dcu-inference-cookbook">HYGON-AI DCU inference cookbook</a> (open deployment guides)</p>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="Iluvatar"></a></p>
-<div align="center"><h3><a href="https://www.iluvatar.com/">Iluvatar CoreX (天数智芯)</a></h3></div>
+<div align="center" class="vendor-title"><h3><a href="https://www.iluvatar.com/">Iluvatar CoreX (天数智芯)</a></h3></div>
 
 <p><strong><a href="https://www.scmp.com/tech/big-tech/article/3341368/iluvatar-corex-targets-nvidias-rubin-gpu-road-map-amid-china-chip-push">Iluvatar CoreX targets Nvidia’s Rubin with GPU road map</a></strong></p>
 <blockquote>
   <p>Jan 2026 — Hong Kong-listed Iluvatar CoreX outlined a multi-generation GPU architecture roadmap (Tianshu / Tianxuan / Tianji / Tianquan messaging) aiming to close the gap with Nvidia Hopper→Blackwell→Rubin class platforms, with BI-V150-class products already in AI training/inference deployments.</p>
 </blockquote>
 
-<div align="center"><h3> </h3></div>
-
 <p><a name="AIChipCompilers"></a></p>
 
-<div align="center"><h2>AI Chip Compilers</h2></div>
+<div align="center" class="section-title"><h2 id="compilers-heading">AI Chip Compilers</h2></div>
+<div class="resource-list">
 
-<p><HR>
+<hr class="section-rule" />
 <strong>Actively used / widely referenced (2024–2026)</strong><br>
 1. <a href="https://mlir.llvm.org/">MLIR</a> — Multi-Level Intermediate Representation (LLVM)<br>
 2. <a href="https://tvm.apache.org/">Apache TVM</a> — end-to-end ML compiler stack<br>
@@ -1531,13 +1294,14 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 14. <a href="http://tensor-compiler.org/">TACO (Tensor Algebra Compiler)</a><br>
 15. <a href="https://github.com/NervanaSystems/ngraph">Intel nGraph</a> (archived)<br>
 
-<div align="center"><h3> </h3></div>
+</div>
 
 <p><a name="AIChipBenchmarks"></a></p>
 
-<div align="center"><h2>AI Chip Benchmarks</h2></div>
+<div align="center" class="section-title"><h2 id="benchmarks-heading">AI Chip Benchmarks</h2></div>
+<div class="resource-list">
 
-<p><HR>
+<hr class="section-rule" />
 
 1. <a href="https://mlcommons.org/benchmarks/">MLPerf (MLCommons)</a> — industry-standard training, inference, HPC, and client benchmarks.<br>
 <strong>Latest results (2026):</strong>
@@ -1550,15 +1314,15 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 5. <a href="https://www.eembc.org/mlmark/">EEMBC MLMark</a><br>
 6. <a href="https://github.com/AIIABenchmark/AIIA-DNN-benchmark">AIIA DNN Benchmark</a><br>
 
-<div align="center"><h3> </h3></div>
+</div>
 
 <p><a name="Reference"></a></p>
 
-<div align="center"><h2>Reference</h2></div>
+<div align="center" class="section-title"><h2>Reference</h2></div>
 
-<p><HR>
+<hr class="section-rule" />
 
-<div align="center"><h3> </h3></div>
+<div class="resource-list">
       
 1. <a href="https://meanderful.blogspot.jp/2017/06/fpgas-and-ai-processors-dnn-and-cnn-for.html">FPGAs and AI processors: DNN and CNN for all</a><br>
 2. <a href="http://www.nanalyze.com/2017/05/12-ai-hardware-startups-new-ai-chips/">12 AI Hardware Startups Building New AI Chips</a><br>
@@ -1569,6 +1333,8 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 7. <strong><a href="https://birenresearch.github.io/AIChip_Paper_List/">AI Chip Paper List</a></strong><br>
 8. <strong><a href="https://khairy2011.medium.com/tpu-vs-gpu-vs-cerebras-vs-graphcore-a-fair-comparison-between-ml-hardware-3f5a19d89e38">TPU vs GPU vs Cerebras vs Graphcore: A Fair Comparison between ML Hardware</a></strong><br>
 
+</div>
+
 <div align="center">
 <a href="http://www.reliablecounter.com" target="_blank"><img src="http://www.reliablecounter.com/count.php?page=https://basicmi.github.io/AI-Chip/&digit=style/plain/3/&reloads=1" alt="laptop" title="laptop" border="0"></a>
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diff --git a/_config.yml b/_config.yml
index cc35c1d..db1f39c 100644
--- a/_config.yml
+++ b/_config.yml
@@ -1 +1,6 @@
-theme: jekyll-theme-modernist
\ No newline at end of file
+title: AI Chip (ICs and IPs)
+description: A curated directory of ICs and IPs for AI, machine learning, and deep learning — covering hyperscaler custom silicon, GPUs/NPUs, IP vendors, startups, and China accelerators.
+theme: jekyll-theme-modernist
+show_downloads: false
+plugins:
+  - jekyll-seo-tag
diff --git a/assets/css/style.scss b/assets/css/style.scss
new file mode 100644
index 0000000..5290c92
--- /dev/null
+++ b/assets/css/style.scss
@@ -0,0 +1,255 @@
+---
+---
+
+@import "{{ site.theme }}";
+
+/* AI-Chip directory — readability & structure overlays on modernist */
+
+@import url("https://fonts.googleapis.com/css2?family=Source+Sans+3:wght@400;600;700&family=Source+Serif+4:opsz,wght@8..60,600;8..60,700&display=swap");
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From 1a133b8f3040471faeb16d44bd16cb35db41870d Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 02:58:28 +0000
Subject: [PATCH 06/13] Prune defunct vendors, keep post-2023 news, refresh
 NVIDIA/TPU history

Remove shut-down or pivoted AI-chip entries, clear pre-2024 blurbs for
other orgs, document full NVIDIA GPU and Google TPU lineages, and add
AI Chip Landscape v0.8 (2026).

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md                           | 1057 ++++++---------------------
 resource/AI_Chip_Landscape_v0p8.png |  Bin 0 -> 104563 bytes
 2 files changed, 204 insertions(+), 853 deletions(-)
 create mode 100644 resource/AI_Chip_Landscape_v0p8.png

diff --git a/README.md b/README.md
index 338c451..62f5e35 100644
--- a/README.md
+++ b/README.md
@@ -1,70 +1,52 @@
-<header class="page-header" align="center">
+<header class="page-header" id="top" align="center">
 <h1 class="site-brand">AI Chip (ICs and IPs)</h1>
 <div class="hero-art"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI-chips.png" loading="lazy" alt="AI chips illustration" /></div>
 <p class="editor-line">Editor <a href="https://www.linkedin.com/in/shan-tang-27342510/"><strong>S.T.</strong></a> (LinkedIn)</p>
 <p class="wechat-line"><strong>WeChat / 微信公众号</strong> · <a href="https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&amp;__biz=MzI3MDQ2MjA3OA==&amp;scene=1&amp;album_id=1374108991751782402&amp;count=3#wechat_redirect">StarryHeavensAbove</a> — AI chip articles</p>
 <div class="wechat-qr"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/qrcode_for_weichat_258.jpg" height="100" loading="lazy" alt="WeChat QR code" /></div>
-<div class="landscape-map"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI_Chip_Landscape_v0p7.png" loading="lazy" alt="AI Chip Landscape map" /></div>
+<div class="landscape-map"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI_Chip_Landscape_v0p8.png" loading="lazy" alt="AI Chip Landscape v0.8 (2026)" /></div>
 <p class="page-intro">A living directory of AI accelerators (ICs &amp; IPs) — hyperscaler custom silicon, GPU/NPU vendors, IP houses, startups, and China domestic accelerators. Prefer official / primary sources; performance claims are often vendor-reported.</p>
 </header>
 
  
 <div align="center" class="section-title"><h2>Latest updates</h2></div>
-<p class="refresh-note" align="center"><em>Last refreshed: July 2026 — major vendor coverage updated for the generative-AI / LLM accelerator era.</em></p>
+<p class="refresh-note" align="center"><em>Last refreshed: July 2026 — defunct vendors removed; pre-2024 entries cleared (except NVIDIA/Google lineage); landscape map v0.8.</em></p>
 <hr class="section-rule" />
 
 <ul class="updates-list">
-<li><strong>NVIDIA</strong>: <a href="#Nvidia">Vera Rubin</a> platform (CES/GTC 2026) and <a href="#Nvidia">Blackwell</a> (B200 / GB200 NVL72).</li>
-<li><strong>AMD</strong>: <a href="#AMD">Instinct MI350</a> series (CDNA 4) and Meta partnership for Helios / MI450-class GPUs.</li>
-<li><strong>Intel</strong>: <a href="#Habana">Gaudi 3</a> AI accelerator for enterprise GenAI training and inference.</li>
-<li><strong>Google</strong>: Cloud TPU <a href="#Google">Trillium (v6e)</a> and <a href="#Google">Ironwood (TPU7x)</a>.</li>
-<li><strong>Amazon AWS</strong>: <a href="#Amazon_AWS">Trainium2</a> / Trn2 UltraServers and Inferentia2.</li>
-<li><strong>Microsoft</strong>: <a href="#Microsoft">Azure Maia 100</a> custom AI accelerator.</li>
-<li><strong>Meta</strong>: <a href="#Facebook">MTIA</a> custom training &amp; inference accelerators (multi-generation roadmap).</li>
-<li><strong>Cerebras</strong>: <a href="#Cerebras">WSE-3 / CS-3</a> wafer-scale engine.</li>
-<li><strong>Groq</strong>: LPU inference focus; technology licensed / team joined <a href="#Groq">NVIDIA</a> (late 2025).</li>
-<li><strong>Benchmarks</strong>: Links updated to <a href="#AIChipBenchmarks">MLPerf Inference v6.0</a> and <a href="#AIChipBenchmarks">Training v6.0</a>.</li>
-<li><strong>Compilers</strong>: Refreshed <a href="#AIChipCompilers">AI Chip Compilers</a> list (Triton, IREE, TensorRT-LLM, etc.).</li>
-<li><strong>Apple</strong>: <a href="#Apple">M4 / M5</a> Apple silicon Neural Engine &amp; GPU Neural Accelerators.</li>
-<li><strong>Samsung</strong>: <a href="#Samsung">Exynos 2500</a> on-device NPU (3nm GAA).</li>
-<li><strong>Qualcomm</strong>: <a href="#Qualcomm">Snapdragon 8 Elite</a> Hexagon NPU for on-device GenAI.</li>
-<li><strong>China accelerators</strong>: <a href="#China_AI_Chips">Huawei Ascend</a>, Cambricon, Biren, Moore Threads, <a href="#MetaX">MetaX</a>, <a href="#Hygon">Hygon DCU</a>, <a href="#Iluvatar">Iluvatar CoreX</a>, Enflame, Horizon.</li>
-<li><strong>Alibaba</strong>: T-Head <a href="#Alibaba">PPU / Zhenwu</a> cloud AI chips.</li>
-<li><strong>Startups</strong>: added <a href="#Etched">Etched</a> (transformer ASIC / Sohu).</li>
-<li><strong>MediaTek</strong>: <a href="#MediaTek">Dimensity 9400</a> NPU 890 / agentic on-device AI.</li>
-<li><strong>Broadcom / OpenAI</strong>: custom AI <a href="#Broadcom">XPU</a> partner; <a href="#OpenAI">Jalapeño</a> inference ASIC.</li>
-<li><strong>Marvell</strong>: noted role as custom AI ASIC partner (Trainium / Maia class programs).</li>
-<li><strong>Anthropic</strong>: multi-cloud compute on <a href="#Anthropic">TPU / Trainium / GPU</a> (Google–Broadcom GW-scale deals).</li>
-<li><strong>Graphcore</strong>: <a href="#Graphcore">acquired by SoftBank</a> (2024); continues IPU / next-gen work inside SoftBank.</li>
-<li><strong>Edge / ADAS</strong>: <a href="#SiMa">SiMa Modalix</a> Physical AI; <a href="#BlackSesame">Black Sesame Huashan</a> automotive SoCs.</li>
-<li><strong>Tesla</strong>: <a href="#Tesla">AI5 tape-out</a> (2026); Dojo / AI4 lineage retained as historical context.</li>
-<li><strong>FuriosaAI</strong>: <a href="#Furiosa">RNGD</a> mass production + Broadcom next-gen partnership; Equinix Europe deploy.</li>
-<li><strong>Hailo</strong>: <a href="#Hailo">Hailo-10H</a> edge GenAI accelerator (LLM/VLM) generally available.</li>
-<li><strong>Arm</strong>: <a href="#ARM">Ethos-U85</a> micro-NPU with native transformer support (edge / IoT GenAI).</li>
-<li><strong>SambaNova</strong>: <a href="#SambaNova">SN50 RDU</a> agentic inference chip + Intel collaboration / Series E.</li>
-<li><strong>Mythic</strong>: <a href="#Mythic">$125M Series D</a> + Videantis acquisition for hybrid analog/digital AI platform.</li>
-<li><strong>Pages UX</strong>: HTML structure cleanup + custom CSS on GitHub Pages (section chrome, shortcut table, vendor logo blocks).</li>
+<li><strong>Hygiene</strong>: removed shut-down / pivoted AI-chip vendors; cleared pre-2024 news for other orgs.</li>
+<li><strong>NVIDIA</strong>: full GPU AI lineage CUDA/Pascal → Volta → Hopper → Blackwell → <a href="#Nvidia">Rubin</a>.</li>
+<li><strong>Google</strong>: full <a href="#Google">TPU</a> lineage v1 → Trillium (v6e) → Ironwood (v7).</li>
+<li><strong>AMD / Intel</strong>: <a href="#AMD">MI350</a>; <a href="#Habana">Gaudi 3</a>.</li>
+<li><strong>Custom silicon</strong>: AWS <a href="#Amazon_AWS">Trainium2</a>, Azure <a href="#Microsoft">Maia</a>, Meta <a href="#Facebook">MTIA</a>, <a href="#OpenAI">Jalapeño</a>, <a href="#Anthropic">Anthropic</a> deals.</li>
+<li><strong>Edge</strong>: <a href="#SiMa">SiMa</a>, <a href="#Hailo">Hailo-10H</a>, <a href="#Tesla">Tesla AI5</a>, <a href="#BlackSesame">Black Sesame</a>.</li>
+<li><strong>Active startups</strong>: <a href="#Cerebras">Cerebras</a>, <a href="#Furiosa">Furiosa</a>, <a href="#Etched">Etched</a>, <a href="#SambaNova">SambaNova</a>, <a href="#Mythic">Mythic</a>, <a href="#Kinara">Kinara</a>, …</li>
+<li><strong>China</strong>: <a href="#China_AI_Chips">Ascend / Cambricon / Biren / MetaX / Hygon / Iluvatar</a> …</li>
+<li><strong>Map</strong>: regenerated AI Chip Landscape <strong>v0.8</strong> (2026).</li>
 </ul>
 
 <div align="center" class="section-title"><h2>Shortcut</h2></div>
 <hr class="section-rule" />
 <table class="shortcut-table">
   <tr>
-    <th><a href="#IC_Vendors">IC Vendors</a></th><td><a href="#Intel">Intel</a>, <a href="#Qualcomm">Qualcomm</a>, <a href="#Nvidia">Nvidia</a>, <a href="#Samsung">Samsung</a>, <a href="#AMD">AMD</a>, <a href="#MediaTek">MediaTek</a>, <a href="#IBM">IBM</a>, <a href="#Marvell">Marvell</a>, <a href="#Broadcom">Broadcom</a></td>
+    <th><a href="#IC_Vendors">IC Vendors</a></th>
+    <td><a href="#Nvidia">NVIDIA</a>, <a href="#Intel">Intel</a>, <a href="#Habana">Gaudi</a>, <a href="#AMD">AMD</a>, <a href="#Qualcomm">Qualcomm</a>, <a href="#Samsung">Samsung</a>, <a href="#MediaTek">MediaTek</a>, <a href="#Marvell">Marvell</a>, <a href="#Broadcom">Broadcom</a></td>
   </tr>
   <tr>
-    <th><a href="#Tech_Giants">Tech Giants & HPC Vendors</a></th><td><a href="#Google">Google</a>, <a href="#Amazon_AWS">Amazon_AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#OpenAI">OpenAI</a>, <a href="#Anthropic">Anthropic</a>, <a href="#Alibaba">Alibaba Group</a>, <a href="#Tencent_Cloud">Tencent Cloud</a>, <a href="#Baidu">Baidu</a>, <a href="#Fujitsu">Fujitsu</a>, <a href="#Nokia">Nokia</a>, <a href="#Facebook">Meta (Facebook)</a>, <a href="#Tesla">Tesla</a></td>
+    <th><a href="#Tech_Giants">Tech Giants &amp; HPC</a></th>
+    <td><a href="#Google">Google</a>, <a href="#Amazon_AWS">AWS</a>, <a href="#Microsoft">Microsoft</a>, <a href="#Apple">Apple</a>, <a href="#OpenAI">OpenAI</a>, <a href="#Anthropic">Anthropic</a>, <a href="#Alibaba">Alibaba</a>, <a href="#Baidu">Baidu</a>, <a href="#Tencent_Cloud">Tencent</a>, <a href="#Facebook">Meta</a>, <a href="#Tesla">Tesla</a></td>
   </tr>
   <tr>
-    <th><a href="#IP_Vendors">IP Vendors</a></th><td><a href="#ARM">ARM</a>, <a href="#Synopsys">Synopsys</a>, <a href="#Imagination">Imagination</a>, <a href="#CEVA">CEVA</a>, <a href="#Cadence">Cadence</a>, <a href="#VeriSilicon">VeriSilicon</a></td>
+    <th><a href="#IP_Vendors">IP Vendors</a></th>
+    <td><a href="#ARM">Arm</a>, <a href="#Synopsys">Synopsys</a>, <a href="#Imagination">Imagination</a>, <a href="#CEVA">CEVA</a>, <a href="#Cadence">Cadence</a>, <a href="#VeriSilicon">VeriSilicon</a></td>
   </tr>
-  <tr>  
+  <tr>
     <th><a href="#Startups">Startups</a></th>
-    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Etched">Etched</a>, <a href="#Groq">Groq</a>, <a href="#SambaNova">SambaNova</a>, <a href="#Mythic">Mythic</a>, <a href="#Hailo">Hailo</a>, <a href="#Furiosa">FuriosaAI</a>, <a href="#d-matrix">d-Matrix</a>, <a href="#SiMa">SiMa</a>, … (full list in section IV)</td>
+    <td><a href="#Cerebras">Cerebras</a>, <a href="#Graphcore">Graphcore</a>, <a href="#Tenstorrent">Tenstorrent</a>, <a href="#Etched">Etched</a>, <a href="#Groq">Groq</a>, <a href="#SambaNova">SambaNova</a>, <a href="#Mythic">Mythic</a>, <a href="#Hailo">Hailo</a>, <a href="#Furiosa">FuriosaAI</a>, <a href="#Kinara">Kinara</a>, <a href="#SiMa">SiMa</a>, <a href="#d-matrix">d-Matrix</a>, <a href="#Blaize">Blaize</a>, …</td>
   </tr>
   <tr>
     <th><a href="#China_AI_Chips">China AI Accelerators</a></th>
-    <td><a href="#HuaweiAscend">Huawei Ascend</a>, <a href="#Cambricon">Cambricon</a>, <a href="#Biren">Biren</a>, <a href="#MooreThreads">Moore Threads</a>, <a href="#MetaX">MetaX</a>, <a href="#Hygon">Hygon</a>, <a href="#Iluvatar">Iluvatar CoreX</a>, <a href="#Enflame">Enflame</a>, <a href="#Horizon">Horizon Robotics</a>, <a href="#BlackSesame">Black Sesame</a>, <a href="#Alibaba">Alibaba T-Head</a>, <a href="#Baidu">Baidu Kunlun</a></td>
+    <td><a href="#HuaweiAscend">Huawei Ascend</a>, <a href="#Cambricon">Cambricon</a>, <a href="#Biren">Biren</a>, <a href="#MooreThreads">Moore Threads</a>, <a href="#MetaX">MetaX</a>, <a href="#Hygon">Hygon</a>, <a href="#Iluvatar">Iluvatar</a>, <a href="#Enflame">Enflame</a>, <a href="#Horizon">Horizon</a>, <a href="#BlackSesame">Black Sesame</a></td>
   </tr>
 </table>
 
@@ -72,65 +54,58 @@
 <hr class="section-rule" />
 
 <a name="Nvidia"></a>
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nvidia_logo.png" height="50" loading="lazy" alt="NVIDIA" /> </div>
-<div align="center"><h3>GPU</h3></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nvidia_logo.png" height="50" loading="lazy" alt="NVIDIA" /></div>
+<div align="center" class="vendor-title"><h3>NVIDIA — GPU / AI accelerator lineage</h3></div>
+
+<p>Architecture history for AI/HPC (CUDA era → rack-scale platforms). Figures are vendor-reported.</p>
+
+<p><strong>CUDA foundation (pre–Tensor Core)</strong></p>
+<ul>
+<li><strong>Tesla arch (2006) + CUDA (2007)</strong> — unified shaders / GPGPU; GPU becomes a general accelerator.</li>
+<li><strong>Fermi (2010)</strong> — caches, ECC, stronger HPC path.</li>
+<li><strong>Kepler (2012)</strong> — Hyper-Q / dynamic parallelism; datacenter Tesla K-series.</li>
+<li><strong>Maxwell (2014)</strong> — major perf/W redesign.</li>
+<li><strong>Pascal (2016) — P100</strong> — HBM2 + NVLink 1.0; first widely adopted datacenter GPU for deep learning at scale.</li>
+</ul>
 
-<p><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Kicks Off the Next Generation of AI With Rubin</a></strong></p>
-<blockquote>
-  <p>CES 2026 — NVIDIA launched the Rubin platform: Vera CPU, Rubin GPU, NVLink 6 Switch, ConnectX-9, BlueField-4 and Spectrum-6, codesigned for agentic AI and large MoE models. vs. Blackwell, NVIDIA claims up to 10x lower inference token cost and 4x fewer GPUs to train MoE models. Partner availability is targeted for H2 2026.</p>
-</blockquote>
+<p><strong>Tensor Core era</strong></p>
+<ul>
+<li><strong>Volta (2017) — V100</strong> — first Tensor Cores (FP16 mixed precision); DGX / early large-model workhorse.</li>
+<li><strong>Turing (2018) — T4</strong> — inference-efficient datacenter SKUs; consumer RTX adds RT + Tensor Cores.</li>
+<li><strong>Ampere (2020) — A100</strong> — TF32 / BF16, structured sparsity, MIG; A100 40/80GB HBM2e.</li>
+<li><strong>Ada Lovelace (2022) — L40 / L4</strong> — inference / visual datacenter SKUs alongside Hopper.</li>
+<li><strong>Hopper (2022) — H100 / H200</strong> — Transformer Engine, FP8, NVLink 4; H200 expands HBM3e for LLM train/serve.</li>
+</ul>
 
-<p><strong><a href="https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing">NVIDIA Blackwell Platform Arrives to Power a New Era of Computing</a></strong></p>
-<blockquote>
-  <p>GTC 2024 — Blackwell succeeds Hopper for trillion-parameter generative AI. The B200 Tensor Core GPU and GB200 Grace Blackwell Superchip power rack-scale GB200 NVL72 systems with fifth-generation NVLink, targeting large-scale training and real-time LLM inference.</p>
-</blockquote>
+<p><strong>Platform / rack-scale era (2024–2026)</strong></p>
+<ul>
+<li><strong>Blackwell (2024) — B200 / GB200 NVL72</strong> — multi-die GPU, FP4/FP6 datapaths, NVLink 5; Grace+Blackwell superchips and NVL72 racks.</li>
+<li><strong>Rubin (CES/GTC 2026)</strong> — Vera CPU + Rubin GPU + NVLink 6 Switch + ConnectX-9 / BlueField-4 / Spectrum-6 for agentic / MoE; partner availability targeted H2 2026. NVIDIA also described integrating Groq-class LPU technology into the AI-factory stack.</li>
+</ul>
 
-<p><a href="https://nvidianews.nvidia.com/news/nvidia-microsoft-accelerate-cloud-enterprise-ai">NVIDIA Teams With Microsoft to Build Massive Cloud AI Computer</a></p>
+<p><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Rubin platform announcement</a></strong></p>
 <blockquote>
-  <p>Tens of Thousands of NVIDIA GPUs, NVIDIA Quantum-2 InfiniBand and Full Stack of NVIDIA AI Software Coming to Azure; NVIDIA, Microsoft and Global Enterprises to Use Platform for Rapid, Cost-Effective AI Development and Deployment</p>
-</blockquote> 
+  <p>CES 2026 — Vera Rubin platform for agentic AI and large MoE models; NVIDIA claims large gains in inference token cost and training GPU-efficiency vs. Blackwell.</p>
+</blockquote>
 
-<p><strong><a href="https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth">NVIDIA Hopper Architecture In-Depth</a></strong></p>
-<blockquote>
-  <p>Today during the 2022 NVIDIA GTC Keynote address, NVIDIA CEO Jensen Huang introduced the new NVIDIA H100 Tensor Core GPU based on the new NVIDIA Hopper GPU architecture. This post gives you a look inside the new H100 GPU and describes important new features of NVIDIA Hopper architecture GPUs.</p>
-</blockquote> 
- 
-<p><a href="https://www.anandtech.com/show/16610/nvidia-unveils-grace-a-highperformance-arm-server-cpu-for-use-in-ai-systems">NVIDIA Unveils Grace: A High-Performance Arm Server CPU For Use In Big AI Systems</a></p>
+<p><strong><a href="https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing">NVIDIA Blackwell platform</a></strong></p>
 <blockquote>
-  <p>Kicking off another busy Spring GPU Technology Conference for NVIDIA, this morning the graphics and accelerator designer is announcing that they are going to once again design their own Arm-based CPU/SoC. Dubbed Grace – after Grace Hopper, the computer programming pioneer and US Navy rear admiral – the CPU is NVIDIA’s latest stab at more fully vertically integrating their hardware stack by being able to offer a high-performance CPU alongside their regular GPU wares. According to NVIDIA, the chip is being designed specifically for large-scale neural network workloads, and is expected to become available in NVIDIA products in 2023.</p>
+  <p>GTC 2024 — B200 Tensor Core GPU and GB200 Grace Blackwell Superchip / NVL72 systems for generative AI training and real-time LLM inference.</p>
 </blockquote>
 
+<p><a href="https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth">Hopper architecture in-depth</a> ·
+<a href="https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/">Hopper</a> ·
+<a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">Blackwell</a></p>
+
 <a name="Intel"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Intel_logo.png" height="60" loading="lazy" alt="Intel" /></div>
-
-<a name="Mobileye"></a>
-<div align="center"><h3>Mobileye EyeQ</h3></div>
-> Mobileye is currently developing its fifth generation SoC, the <a href="https://www.mobileye.com/our-technology/evolution-eyeq-chip/">EyeQ®5</a>, to act as the vision central computer performing sensor fusion for Fully Autonomous Driving (Level 5) vehicles that will hit the road in 2020. To meet power consumption and performance targets, EyeQ® SoCs are designed in most advanced VLSI process technology nodes – down to 7nm FinFET in the 5th generation. 
-
-<a name="Loihi 2"></a>
-<div align="center"><h3>Loihi</h3></div>
-<p><a href="https://www.intel.com/content/www/us/en/newsroom/news/intel-unveils-neuromorphic-loihi-2-lava-software.html">Intel Advances Neuromorphic with Loihi 2, New Lava Software Framework and New Partners</a></p>
-<blockquote>
-  <p>Second-generation research chip uses pre-production Intel 4 process, grows to 1 million neurons. Intel adds open software framework to accelerate developer innovation and path to commercialization.</p>
-</blockquote>
-
-<p><a name="Habana"></a></p>
-<div align="center"><h3>Habana</h3></div>
+<div align="center" class="vendor-title"><h3>Intel / Habana Gaudi</h3></div>
+<a name="Habana"></a>
 <p><strong><a href="https://newsroom.intel.com/artificial-intelligence/vision-2024-enterprise-ai-gaudi-3-open-systems-strategy">Intel Unleashes Enterprise AI with Gaudi 3</a></strong></p>
 <blockquote>
   <p>Intel Vision 2024 — Gaudi 3 targets generative AI training and inference with Ethernet scale-out. Intel positions Gaudi 3 for enterprise GenAI price/performance vs. GPU alternatives, with OEM availability through Dell, HPE, Lenovo, Supermicro and cloud partners.</p>
 </blockquote>
 
-<p><strong><a href="https://www.intel.com/content/www/us/en/newsroom/news/vision-2022-habana-gaudi2-greco.html">Intel’s Habana Labs Launches Second-Generation AI Processors for Training and Inferencing</a></strong></p>
-<blockquote>
-  <p>Today at Intel Vision, Intel announced that Habana Labs, its data center team focused on AI deep learning processor technologies, launched its second-generation deep learning processors for training and inference: Habana® Gaudi®2 and Habana® Greco™. These new processors address an industry gap by providing customers with high-performance, high-efficiency deep learning compute choices for both training workloads and inference deployments in the data center while lowering the AI barrier to entry for companies of all sizes.</p>
-</blockquote>
- 
-<p><a href="https://habana.ai/aws-launches-ec2-dl1-instances/">Habana Gaudi debuts in the Amazon EC2 cloud</a></p>
-<blockquote>
-  <p>The primary motivation to create this new training instance class was presented by Andy Jassy in the 2020 re:Invent: “To provide our end-customers with up to 40% better price-performance than the current generation of GPU-based instances.”</p>
-</blockquote>
-
 <a name="Qualcomm"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Qualcomm_logo.png" height="40" loading="lazy" alt="Qualcomm" /></div>
 
@@ -138,34 +113,18 @@
 <blockquote>
   <p>Snapdragon Summit 2024 — custom Oryon CPU plus a redesigned Hexagon NPU (fused scalar/vector/tensor accelerators). Qualcomm cites ~45% faster NPU performance and better perf/W vs. Snapdragon 8 Gen 3 for multimodal on-device generative AI.</p>
 </blockquote>
-
 <p><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Qualcomm Cloud AI 100</a></p>
 <blockquote>
   <p>Cloud/edge inference accelerator focused on power-efficient deployment outside traditional GPU clusters.</p>
 </blockquote>
 
-<a href="https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/karlfreund/2022/11/16/qualcomm-ups-the-snapgragon-ai-game/amp/">Qualcomm Ups The Snapgragon AI Game</a>
-<blockquote>
-  <p>The leader in premium mobile SoCs has applied AI across the entire platform.</p>
-</blockquote>
-
-<strong><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Qualcomm Cloud AI 100</a></strong>
-<blockquote>
-  <p>The Qualcomm Cloud AI 100, designed for AI inference acceleration, addresses unique requirements in the cloud, including power efficiency, scale, process node advancements, and signal processing—facilitating the ability of datacenters to run inference on the edge cloud faster and more efficiently. Qualcomm Cloud AI 100 is designed to be a leading solution for datacenters who increasingly rely on infrastructure at the edge-cloud.</p>
-</blockquote>
-
 <a name="Samsung"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Samsung_logo.png" height="35" loading="lazy" alt="Samsung" /></div>
 <p><strong><a href="https://semiconductor.samsung.com/processor/mobile-processor/exynos-2500/">Samsung Exynos 2500</a></strong></p>
 <blockquote>
-  <p>Flagship mobile SoC on Samsung 3nm GAA process with a substantially upgraded NPU (Samsung cites up to ~59 TOPS and ~39% better on-device AI vs. Exynos 2400) for generative AI and privacy-preserving on-device inference.</p>
+  <p>2025 flagship mobile SoC on Samsung 3nm GAA with a substantially upgraded NPU (Samsung cites up to ~59 TOPS and large on-device AI gains vs. Exynos 2400) for generative AI and privacy-preserving on-device inference.</p>
 </blockquote>
 
-<p><a href="https://news.samsung.com/global/samsung-brings-on-device-ai-processing-for-premium-mobile-devices-with-exynos-9-series-9820-processor">Samsung Brings On-device AI Processing… Exynos 9 Series 9820</a> (historical)</p>
-<blockquote>
-  <p>Earlier on-device AI messaging around Exynos 9820 / 9810-era NPU features for AR/VR and mobile deep learning.</p>
-</blockquote>   
-
 <a name="MediaTek"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/MediaTek_logo.png" height="40" loading="lazy" alt="MediaTek" /></div>
 
@@ -174,79 +133,35 @@
   <p>Oct 2024 — Flagship Dimensity 9400 (TSMC 3nm) pairs an all-big-core CPU with MediaTek’s 8th-generation NPU 890. MediaTek highlights on-device LoRA training, high-quality video generation, MoE LLM support, and the Dimensity Agentic AI Engine (DAE), with claimed large gains in LLM prompt / diffusion performance vs. Dimensity 9300.</p>
 </blockquote>
 
-<p><a href="https://www.mediatek.com/products/smartphones/mediatek-dimensity-9400">Dimensity 9400 product page</a></p>
-
 <a name="AMD"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AMD_logo.png" height="35" loading="lazy" alt="AMD" /></div>
 <p><strong><a href="https://www.amd.com/en/products/accelerators/instinct/mi350.html">AMD Instinct™ MI350 Series GPUs</a></strong></p>
 <blockquote>
   <p>Built on 4th Gen AMD CDNA™ architecture, MI350X / MI355X deliver large HBM3E capacity (up to 288GB) and high bandwidth for generative AI training and inference, with expanded low-precision datatype support (including MXFP6 / MXFP4). MI400-series / Helios rack-scale systems are on AMD’s 2026 roadmap.</p>
 </blockquote>
-
 <p><strong><a href="https://www.amd.com/en/newsroom/press-releases/2026-2-24-amd-and-meta-announce-expanded-strategic-partnersh.html">AMD and Meta Announce Expanded Strategic Partnership to Deploy 6 Gigawatts of AMD GPUs</a></strong></p>
 <blockquote>
   <p>Feb 2026 — AMD and Meta expanded their partnership for multi-generation Instinct GPU deployments, including custom MI450-class GPUs on the Helios rack architecture with ROCm software.</p>
 </blockquote>
 
-<p><a href="https://www.amd.com/en/products/accelerators/instinct.html">AMD Instinct™ MI Series Accelerators</a></p>
-<blockquote>
-  <p>AMD Instinct accelerators target data-center HPC and AI, combining CDNA architecture, Infinity Fabric interconnect, and the open ROCm software stack for training and inference at scale.</p>
-</blockquote>
-
-<p><a name="IBM"></a></p>
-
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/IBM_logo.png" height="40" loading="lazy" alt="IBM" /></div>
-
-<p><a href="https://www.ibm.com/blogs/systems/ibm-telum-processor-the-next-gen-microprocessor-for-ibm-z-and-ibm-linuxone/">Meet the IBM Artificial Intelligence Unit</a></p>
-
-<blockquote>
-  <p>It’s our first complete system-on-chip designed to run and train deep learning models faster and more efficiently than a general-purpose CPU.</p>
-</blockquote>
-
-<p><a href="https://www.ibm.com/blogs/systems/ibm-telum-processor-the-next-gen-microprocessor-for-ibm-z-and-ibm-linuxone/">IBM Telum Processor: the next-gen microprocessor for IBM Z and IBM LinuxONE</a></p>
-
-<blockquote>
-  <p>The 7 nm microprocessor is engineered to meet the demands our clients face for gaining AI-based insights from their data without compromising response time for high volume transactional workloads. </p>
-</blockquote>
-
-<p><a href="https://www.ibm.com/blogs/research/tag/truenorth/">TrueNorth</a> is IBM's Neuromorphic CMOS ASIC developed in conjunction with the DARPA <a href="https://en.wikipedia.org/wiki/SyNAPSE">SyNAPSE</a> program.</p>
-
-<blockquote>
-  <p>It is a manycore processor network on a chip design, with 4096 cores, each one simulating 256 programmable silicon "neurons" for a total of just over a million neurons. In turn, each neuron has 256 programmable "synapses" that convey the signals between them. Hence, the total number of programmable synapses is just over 268 million (228). In terms of basic building blocks, its transistor count is 5.4 billion. Since memory, computation, and communication are handled in each of the 4096 neurosynaptic cores, TrueNorth circumvents the von-Neumann-architecture bottlenecks and is very energy-efficient, consuming 70 milliwatts, about 1/10,000th the power density of conventional microprocessors. <a href="https://en.wikipedia.org/wiki/TrueNorth">Wikipedia</a></p>
-</blockquote>
-
-<p><a href="https://www.research.ibm.com/artificial-intelligence/ai-hardware-center/">AI Hardware Center</a></p>
-<blockquote>
-  <p>"The IBM Research AI Hardware Center is a global research hub headquartered in Albany, New York. The center is focused on enabling next-generation chips and systems that support the tremendous processing power and unprecedented speed that AI requires to realize its full potential.</p>
-</blockquote>
-
-<p><a name="Marvell"></a></p>
-
+<a name="Marvell"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Marvell_logo.png" height="60" loading="lazy" alt="Marvell" /></div>
-
 <p><strong><a href="https://www.marvell.com/products/data-processing-units.html">Custom AI ASICs + DPUs</a></strong></p>
 <blockquote>
-  <p>Beyond OCTEON / ARMADA DPUs for networking infrastructure, Marvell is a major custom AI accelerator design partner for hyperscalers (widely reported programs include AWS Trainium/Inferentia-class and Microsoft Maia-class silicon), complementing Broadcom’s XPU franchise.</p>
+  <p>2024–2026 — Beyond OCTEON / ARMADA DPUs, Marvell is a major custom AI accelerator design partner for hyperscalers (widely reported programs include AWS Trainium/Inferentia-class and Microsoft Maia-class silicon), complementing Broadcom’s XPU franchise.</p>
 </blockquote>
 
-<p><a href="https://www.marvell.com/products/data-processing-units.html">Data Processing Units</a></p>
-<blockquote>
-  <p>Marvell’s OCTEON™, OCTEON™ Fusion and ARMADA® platforms target wireless infrastructure, carrier networks, enterprise and cloud data centers.</p>
-</blockquote>
-
-<p><a name="Broadcom"></a></p>
+<a name="Broadcom"></a>
 <div align="center"><h3>Broadcom</h3></div>
 
 <p><strong><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI and Broadcom announce strategic collaboration for custom AI accelerators</a></strong></p>
 <blockquote>
   <p>Oct 2025 — Multi-year plan to deploy ~10 GW of OpenAI-designed accelerators co-developed with Broadcom (Ethernet scale-up/scale-out), with rack deployments targeted from H2 2026 through 2029.</p>
 </blockquote>
-
 <p><strong><a href="https://www.nasdaq.com/press-release/broadcom-announces-extended-partnership-meta-deploy-technology-support-multi">Broadcom extends partnership with Meta on MTIA custom silicon</a></strong></p>
 <blockquote>
   <p>Apr 2026 — Broadcom’s XPU platform continues as the co-design / implementation backbone for Meta’s multi-generation MTIA accelerators and supporting Ethernet AI networking fabric.</p>
 </blockquote>
-
 <p><a href="https://www.hpcwire.com/2025/09/09/broadcom-steps-up-to-challenge-nvidia-in-ai-chip-arms-race/">Broadcom custom XPUs for hyperscaler AI</a></p>
 <blockquote>
   <p>Broadcom co-designs customer-specific AI accelerators (XPUs) and pairs them with Tomahawk / Jericho Ethernet switching — a long-running model that began with Google TPU generations and now spans multiple frontier labs and cloud providers.</p>
@@ -256,83 +171,64 @@
 
 <hr class="section-rule" />
 
-<p><a name="Google"></a></p>
-
+<a name="Google"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Google_logo.png" height="40" loading="lazy" alt="Google" /></div>
+<div align="center" class="vendor-title"><h3>Google — TPU lineage</h3></div>
 
-<p><strong><a href="https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus">Introducing Trillium, sixth-generation TPUs</a></strong></p>
-<blockquote>
-  <p>Google Cloud’s TPU v6e (Trillium) delivers up to 4.7× peak compute per chip vs. TPU v5e, with doubled HBM capacity/bandwidth and improved energy efficiency for foundation-model training and serving.</p>
-</blockquote>
-
-<p><strong><a href="https://docs.cloud.google.com/tpu/docs/release-notes">Cloud TPU Ironwood (TPU7x) generally available</a></strong></p>
-<blockquote>
-  <p>Ironwood is Google Cloud’s seventh-generation TPU family for large-scale training and inference (LLMs, MoEs, diffusion). See Cloud TPU docs / release notes for TPU7x availability and configurations.</p>
-</blockquote>
-
-<p><a href="https://docs.cloud.google.com/tpu/docs/v6e">TPU v6e (Trillium) architecture documentation</a></p>
+<p>Cloud TPU generation history (Edge TPU / Pixel Tensor noted as related). Specs are Google-reported.</p>
 
-<p><strong><a href="https://www.zdnet.com/article/google-tensor-everything-you-need-to-know-about-the-pixel-6-chip/">Google Tensor: Everything you need to know about the Pixel 6 chip</a></strong></p>
-<blockquote>
-  <p>Google has taken the wraps off its latest Pixel smartphones and, among the changes, the one with the biggest long-term impact is the switch to in-house silicon for the search giant.</p>
-</blockquote>
- 
-<p><a href="https://www.hpcwire.com/2021/05/20/google-launches-tpu-v4-ai-chips/">Google Launches TPU v4 AI Chips</a></p>
-<blockquote>
-  <p>Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I/O virtual conference this week, but it may have been the most important and awaited news from the event.</p>
-</blockquote>
- 
-<p><a href="https://cloud.google.com/tpu">Cloud TPU</a></p>
-<blockquote>
-  <p>Machine learning has produced business and research breakthroughs ranging from network security to medical diagnoses. We built the Tensor Processing Unit (TPU) in order to make it possible for anyone to achieve similar breakthroughs. Cloud TPU is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail. Here’s how you can put the TPU and machine learning to work accelerating your company’s success, especially at scale.</p>
-</blockquote>
- 
-<p><a href="https://cloud.google.com/edge-tpu/">Edge TPU</a></p>
-<blockquote>
-  <p>AI is pervasive today, from consumer to enterprise applications. With the explosive growth of connected devices, combined with a demand for privacy/confidentiality, low latency, and bandwidth constraints, AI models trained in the cloud increasingly need to be run at the edge. Edge TPU is Google’s purpose-built ASIC designed to run AI at the edge. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge.</p>
-</blockquote>
- 
-<p>Other references are:<br>
-<a href="https://mp.weixin.qq.com/s/b22p26_delWfSpy9kDJKhA">Google TPU3 看点</a><br><br>
-<a href="https://mp.weixin.qq.com/s/Kf_L4u7JRxJ8kF3Pi8M5iw">Google TPU 揭密</a><br><br>
-<a href="https://mp.weixin.qq.com/s/lBQyNSNa6-joeLZ_Kq2W8A">Google的神经网络处理器专利</a><br><br>
-<a href="https://mp.weixin.qq.com/s/g-BDlvSy-cx4AKItcWF7jQ">脉动阵列 - 因Google TPU获得新生</a><br><br>
-<a href="https://www.linkedin.com/pulse/should-we-all-embrace-systolic-arrays-chien-ping-lu">Should We All Embrace Systolic Arrays?</a><br></p>
+<p><strong>Early generations</strong></p>
+<ul>
+<li><strong>TPU v1 (2015)</strong> — inference-only systolic MXU (INT8) inside Google production services; public details followed ~2016–2017.</li>
+<li><strong>TPU v2 (2017)</strong> — training + inference, bfloat16, HBM, first multi-chip pods.</li>
+<li><strong>TPU v3 (2018)</strong> — higher compute, liquid cooling at pod scale.</li>
+<li><strong>TPU v4 (2021)</strong> — denser pods, 3D torus + optical circuit switches; backbone for many Google foundation-model trainings.</li>
+</ul>
 
-<p><a name="Amazon_AWS"></a></p>
+<p><strong>Split SKUs &amp; GenAI scale-up</strong></p>
+<ul>
+<li><strong>TPU v5e (2023)</strong> — cost-efficient training/inference at volume.</li>
+<li><strong>TPU v5p (2023)</strong> — high-end large-pod training performance.</li>
+<li><strong>Trillium / TPU v6e (2024)</strong> — larger MXUs; Google cites ~4.7× peak compute/chip vs. v5e, with higher HBM capacity/bandwidth and better energy efficiency.</li>
+<li><strong>Ironwood / TPU v7 (2025)</strong> — inference-leaning generation with native low-precision (FP8-class) paths and very large pod configurations.</li>
+</ul>
 
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Amazon_AWS.png" height="50" loading="lazy" alt="Amazon AWS" /></div>
+<p><strong>Related Google silicon</strong></p>
+<ul>
+<li><strong>Edge TPU</strong> — Coral edge inference ASIC.</li>
+<li><strong>Google Tensor (Pixel)</strong> — mobile SoC ML blocks (separate from Cloud TPU).</li>
+</ul>
 
-<p><strong><a href="https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/">Amazon EC2 Trn2 Instances and Trn2 UltraServers</a></strong></p>
+<p><strong><a href="https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus">Introducing Trillium (TPU v6e)</a></strong></p>
 <blockquote>
-  <p>Powered by AWS Trainium2, Trn2 instances target generative AI training and inference with large HBM capacity and NeuronLink chip-to-chip interconnect. AWS positions Trn2 for strong price/performance vs. GPU instances; UltraServers scale to dozens of Trainium2 chips.</p>
+  <p>Sixth-generation Cloud TPU for foundation-model training and serving; Google cites large per-chip compute and efficiency gains vs. TPU v5e.</p>
 </blockquote>
 
-<p><a href="https://aws.amazon.com/ec2/instance-types/inf2/">Amazon EC2 Inf2 (Inferentia2)</a></p>
+<p><strong><a href="https://docs.cloud.google.com/tpu/docs/release-notes">Ironwood (TPU v7 / TPU7x) availability notes</a></strong></p>
 <blockquote>
-  <p>Inferentia2-powered Inf2 instances improve throughput and latency for large-model inference vs. first-generation Inferentia, with multi-chip scale-out via NeuronLink.</p>
+  <p>Seventh-generation Cloud TPU family for large-scale training and inference (LLMs, MoEs, diffusion).</p>
 </blockquote>
 
-<p><strong><a href="https://aws.amazon.com/cn/machine-learning/trainium/">AWS Trainium</a></strong></p>
-<blockquote>
-  <p>AWS Trainium is the second custom machine learning (ML) chip designed by AWS that provides the best price performance for training deep learning models in the cloud.  Trainium offers the highest performance with the most teraflops (TFLOPS) of compute power for the fastest ML training in Amazon EC2 and enables a broader set of ML applications. The Trainium chip is specifically optimized for deep learning training workloads for applications including image classification, semantic search, translation, voice recognition, natural language processing and recommendation engines.</p>
-</blockquote>
+<p><a href="https://cloud.google.com/transform/ai-specialized-chips-tpu-history-gen-ai">Google: 10 years of TPU history</a> ·
+<a href="https://docs.cloud.google.com/tpu/docs/v6e">TPU v6e architecture docs</a> ·
+<a href="https://cloud.google.com/tpu">Cloud TPU</a> ·
+<a href="https://cloud.google.com/edge-tpu/">Edge TPU</a></p>
 
-<p><a href="https://aws.amazon.com/cn/machine-learning/inferentia/">AWS Inferentia. High performance machine learning inference chip, custom designed by AWS.</a></p>
+<a name="Amazon_AWS"></a>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Amazon_AWS.png" height="50" loading="lazy" alt="Amazon AWS" /></div>
+<p><strong><a href="https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/">Amazon EC2 Trn2 / Trainium2 UltraServers</a></strong></p>
 <blockquote>
-  <p>AWS Inferentia provides high throughput, low latency inference performance at an extremely low cost. Each chip provides hundreds of TOPS (tera operations per second) of inference throughput to allow complex models to make fast predictions. For even more performance, multiple AWS Inferentia chips can be used together to drive thousands of TOPS of throughput. AWS Inferentia will be available for use with Amazon SageMaker, Amazon EC2, and Amazon Elastic Inference.</p>
+  <p>2024–2025 — Trainium2-powered Trn2 instances and UltraServers for generative AI training and inference with NeuronLink scale-up; Inferentia2 (Inf2) continues for cost-efficient LLM serving.</p>
 </blockquote>
 
-<p><a name="Microsoft"></a></p>
-
+<a name="Microsoft"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Microsoft_logo.png" height="60" loading="lazy" alt="Microsoft" /></div>
-
-<p><strong><a href="https://azure.microsoft.com/en-us/blog/azure-maia-for-the-era-of-ai-from-silicon-to-software-to-systems/">Azure Maia for the era of AI: From silicon to software to systems</a></strong></p>
+<p><strong><a href="https://azure.microsoft.com/en-us/blog/azure-maia-for-the-era-of-ai-from-silicon-to-software-to-systems/">Azure Maia for the era of AI</a></strong></p>
 <blockquote>
-  <p>Microsoft’s first in-house AI accelerator, Azure Maia 100 (5nm, advanced packaging), is co-designed for Azure AI / Copilot-class cloud workloads, with a software stack integrating PyTorch and ONNX Runtime.</p>
+  <p>2024 — Microsoft’s first in-house AI accelerator, Azure Maia 100 (5nm, advanced packaging), co-designed for Azure AI / Copilot-class cloud workloads, with a software stack integrating PyTorch and ONNX Runtime.</p>
 </blockquote>
 
-<p><a name="OpenAI"></a></p>
+<a name="OpenAI"></a>
 <div align="center"><h3>OpenAI</h3></div>
 
 <p><strong><a href="https://openai.com/index/openai-broadcom-jalapeno-inference-chip/">OpenAI and Broadcom unveil Jalapeño LLM inference chip</a></strong> · <a href="https://techcrunch.com/2026/06/24/openai-unveils-its-first-custom-chip-built-by-broadcom/">TechCrunch coverage</a></p>
@@ -340,9 +236,7 @@
   <p>Jun 2026 — OpenAI’s first custom “Intelligence Processor,” Jalapeño, is an inference-optimized ASIC co-developed with Broadcom (networking + silicon implementation) for lower-cost, higher-utilization LLM serving. First step in a multi-generation platform with initial deployments targeted by end of 2026.</p>
 </blockquote>
 
-<p><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI–Broadcom 10 GW accelerator collaboration</a></p>
-
-<p><a name="Anthropic"></a></p>
+<a name="Anthropic"></a>
 <div align="center"><h3>Anthropic</h3></div>
 
 <p><strong><a href="https://www.anthropic.com/news/google-broadcom-partnership-compute">Anthropic expands partnership with Google and Broadcom for next-generation compute</a></strong></p>
@@ -350,10 +244,7 @@
   <p>Apr 2026 — Expanded agreement for multiple gigawatts of next-generation Google TPU capacity (Broadcom SEC filings cite ~3.5 GW), expected online from 2027, on top of prior &gt;1 GW TPU commitments. Anthropic emphasizes a multi-hardware strategy: AWS Trainium (incl. Project Rainier), Google TPUs, and NVIDIA GPUs.</p>
 </blockquote>
 
-<p><a href="https://techcrunch.com/2026/04/07/anthropic-compute-deal-google-broadcom-tpus/">TechCrunch: Anthropic ups compute deal with Google and Broadcom</a> ·
-<a href="https://www.cnbc.com/2025/10/23/anthropic-google-cloud-deal-tpu.html">CNBC: Oct 2025 Google TPU cloud deal</a></p>
-
-<p><a name="Apple"></a></p>
+<a name="Apple"></a>
 
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Apple_logo.png" height="60" loading="lazy" alt="Apple" /></div>
 
@@ -361,18 +252,16 @@
 <blockquote>
   <p>Oct 2025 — M5 (3nm) adds Neural Accelerators in each GPU core and a faster 16-core Neural Engine, targeting on-device Apple Intelligence and local AI workloads across MacBook Pro, iPad Pro, and Vision Pro.</p>
 </blockquote>
-
 <p><strong><a href="https://www.apple.com/newsroom/2026/03/apple-debuts-m5-pro-and-m5-max-to-supercharge-the-most-demanding-pro-workflows/">Apple debuts M5 Pro and M5 Max</a></strong></p>
 <blockquote>
   <p>Mar 2026 — Fusion Architecture scales GPU AI compute and unified memory bandwidth (up to 128GB / ~614GB/s on M5 Max) for local LLM prompt processing and pro creative AI workflows.</p>
 </blockquote>
-
 <p><a href="https://www.apple.com/newsroom/2024/05/apple-introduces-m4-chip/">Apple introduces M4 chip</a></p>
 <blockquote>
   <p>May 2024 — M4 Neural Engine up to ~38 TOPS, with CPU ML accelerators and high-bandwidth unified memory for on-device AI on iPad Pro and later Macs.</p>
 </blockquote>
 
-<p><a name="Alibaba"></a></p>
+<a name="Alibaba"></a>
 
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/alibaba_logo.png" height="60" loading="lazy" alt="Alibaba" /></div>
 
@@ -381,77 +270,32 @@
   <p>Sep 2025 — Alibaba’s T-Head Parallel Processing Unit (PPU / Zhenwu family) targets cloud AI training and inference as a domestic alternative under export-control constraints. Reports cite ~96GB HBM2e-class memory and high chip-to-chip bandwidth; deployed in large China Unicom / Alibaba Cloud clusters.</p>
 </blockquote>
 
-<p><a href="https://www.alibabacloud.com/blog/alibaba-unveils-new-ai-chip-flagship-model-and-rebuilt-cloud-stack-ai-for-agentic-era_603151">Alibaba Cloud Summit: Zhenwu M890 and agentic AI stack</a></p>
-<blockquote>
-  <p>T-Head’s newer Zhenwu-class accelerators (e.g. M890 messaging) emphasize larger on-package memory, low-precision formats (down to FP4), and cluster interconnect / SAIL software for agentic cloud workloads.</p>
-</blockquote>
-
-<p><a href="https://medium.com/syncedreview/alibabas-new-ai-chip-can-process-nearly-80k-images-per-second-63412dec22a3">Alibaba’s Hanguang 800</a> (historical, 2019)</p>
-<blockquote>
-  <p>Pingtouge / T-Head’s first AI inference ASIC, highlighted for high images-per-second inference throughput at Apsara Conference 2019.</p>
-</blockquote>
-
-<p><a name="Tencent_Cloud"></a></p>
-
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tencent_Cloud_logo.png" height="30" loading="lazy" alt="Tencent Cloud" /></div>
-
-<p><a href="https://www.datacenterdynamics.com/en/news/tencent-reveals-three-data-center-chips-for-ai-video-transcoding-and-networking/">Tencent reveals three data center chips - for AI, video transcoding, and networking</a></p>
-<blockquote>
-  <p>The company claims that the Zixiao AI chip is twice as good as comparable competing products, video transcoding chip Canghai was 30 percent better, and SmartNIC Xuanling was apparently four times as good. It did not provide external benchmarks or specific product details.</p>
-</blockquote>
-
-<p><br />
-<a name="Baidu"></a></p>
+<p>
 
+<a name="Baidu"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Baidu_logo.png" height="40" loading="lazy" alt="Baidu" /></div>
-
-<p><strong><a href="https://www.reuters.com/technology/baidu-says-2nd-gen-kunlun-ai-chips-enter-mass-production-2021-08-18/">Baidu Kunlun / Kunlunxin AI accelerators</a></strong></p>
-<blockquote>
-  <p>Baidu’s Kunlunxin line (Kunlun chips) continues as a domestic cloud/training &amp; inference option alongside hyperscaler custom silicon. 2nd-gen Kunlun entered mass production in 2021; later generations target large-model clusters inside China.</p>
-</blockquote>
-
-<p><a href="https://www.reuters.com/technology/baidu-says-2nd-gen-kunlun-ai-chips-enter-mass-production-2021-08-18/">Baidu says 2nd-gen Kunlun AI chips enter mass production</a> (2021)</p>
+<p><strong><a href="https://www.baidu.com/">Baidu Kunlunxin</a></strong></p>
 <blockquote>
-  <p>Early mass-production milestone for Baidu’s second-generation Kunlun AI chips.</p>
+  <p>2024–2026 — Kunlunxin P800-class GPUs and follow-ons remain a core China training/inference option in Baidu Cloud / ERNIE deployments, with continued software stack updates for large-model serving.</p>
 </blockquote>
 
-<p><a name="Fujitsu"></a></p>
-
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Fujitsu_logo.png" height="40" loading="lazy" alt="Fujitsu" /></div>
-
-<blockquote>
-  <p>This <a href="https://www.nextplatform.com/2017/08/09/fujitsu-bets-deep-leaning-hpc-divergence/">DLU that Fujitsu is creating</a> is done from scratch, and it is not based on either the Sparc or ARM instruction set and, in fact, it has its own instruction set and a new data format specifically for deep learning, which were created from scratch. 
-  Japanese computing giant Fujitsu. Which knows a thing or two about making a very efficient and highly scalable system for HPC workloads, as evidenced by the K supercomputer, does not believe that the HPC and AI architectures will converge. Rather, the company is banking on the fact that these architectures will diverge and will require very specialized functions. </p>
-</blockquote>
-
-<p><a name="Nokia"></a></p>
-
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nokia_logo.png" height="30" loading="lazy" alt="Nokia" /></div>
-
+<a name="Tencent_Cloud"></a>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tencent_Cloud_logo.png" height="30" loading="lazy" alt="Tencent Cloud" /></div>
+<p><strong>Tencent Cloud AI accelerators (Zixiao / custom)</strong></p>
 <blockquote>
-  <p>Nokia has developed the <a href="https://networks.nokia.com/5g/reefshark">ReefShark chipsets</a> for its 5G network solutions. AI is implemented in the ReefShark design for radio and embedded in the baseband to use augmented deep learning to trigger smart, rapid actions by the autonomous, cognitive network, enhancing network optimization and increasing business opportunities.</p>
+  <p>2024–2026 — Tencent continues deploying proprietary and partner AI accelerators across Tencent Cloud for recommendation, multimodal, and LLM inference/training inside its China cloud footprint.</p>
 </blockquote>
 
-<p><a name="Facebook"></a></p>
+<a name="Facebook"></a>
 
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/facebook_logo.png" height="50" loading="lazy" alt="Meta" /></div>
 
-<p><strong><a href="https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/">Our next generation Meta Training and Inference Accelerator (MTIA)</a></strong></p>
-<blockquote>
-  <p>Meta’s custom MTIA family targets ranking/recommendation and broader AI workloads. Newer generations expand coverage toward GenAI inference and training, complementing GPU capacity in Meta’s AI infrastructure.</p>
-</blockquote>
-
 <p><strong><a href="https://ai.meta.com/blog/meta-mtia-scale-ai-chips-for-billions/">Four MTIA Chips in Two Years: Scaling AI Experiences for Billions</a></strong></p>
 <blockquote>
   <p>Meta describes a multi-generation MTIA roadmap (including MTIA 300–500 class chips) with modular chiplet designs and accelerating deployment cadence through 2026–2027.</p>
 </blockquote>
 
-<p><a href="https://www.reuters.com/technology/facebook-developing-machine-learning-chip-information-2021-09-09/">Facebook developing machine learning chip - The Information</a> (historical, 2021)</p>
-<blockquote>
-  <p>Early reporting on Meta/Facebook’s in-house ML chip efforts for recommendation workloads.</p>
-</blockquote>
-
-<p><a name="Tesla"></a></p>
+<a name="Tesla"></a>
 
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tesla_logo.png" height="60" loading="lazy" alt="Tesla" /></div>
 
@@ -460,23 +304,11 @@
   <p>Apr 2026 — Elon Musk announced Tesla’s next-generation AI5 inference/training SoC has taped out, with AI6 / Dojo3 also mentioned as in progress. Volume production and broad vehicle deployment remain gated (reports point to mid–late 2027+); near-term vehicles continue on AI4 / interim upgrades, while AI5 is also positioned for Optimus and internal clusters.</p>
 </blockquote>
 
-<p><a href="https://www.trendforce.com/news/2026/07/13/news-samsung-reportedly-completes-tesla-ai5-tape-out-paving-the-way-for-2nm-ramp-at-taylor-fab/">TrendForce: Samsung foundry path for AI5 / Taylor 2nm messaging</a></p>
-
-<p><strong><a href="https://www.forbes.com/sites/jamesmorris/2022/10/06/teslas-biggest-news-at-ai-day-was-the-dojo-supercomputer-not-the-optimus-robot/">Tesla AI Day — Dojo supercomputer</a></strong> (historical)</p>
-<blockquote>
-  <p>Elon Musk played AI Day to the crowd with the focus on the Optimus humanoid robot. But while this could have a huge impact on our lives and society if it does enter mass production at the price Musk suggested ($20,000), another part of the presentation will have more immediate effects. That was the status report on the Dojo supercomputer. It could really change the world much more quickly than a bipedal bot.</p>
-</blockquote>
-
-<p><a href="https://semianalysis.com/tesla-dojo-ai-super-computer-unique-packaging-and-chip-design-allow-an-order-magnitude-advantage-over-competing-ai-hardware/">Tesla Dojo – Unique Packaging and Chip Design Allow An Order Magnitude Advantage Over Competing AI Hardware</a></p>
-<blockquote>
-  <p>Tesla hosted their AI Day and revealed the innerworkings of their software and hardware infrastructure. Part of this reveal was the previously teased Dojo AI training chip. Tesla claims their D1 Dojo chip has a GPU level compute, CPU level flexibility, with networking switch IO. </p>
-</blockquote>
-
 <div align="center" class="section-title"><h2><a name="IP_Vendors"></a>III. Traditional IP Vendors</h2></div>
 
 <hr class="section-rule" />
 
-<p><a name="ARM"></a></p>
+<a name="ARM"></a>
 
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ARM_logo.png" height="30" loading="lazy" alt="Arm" /></div>
 
@@ -487,58 +319,48 @@
 
 <p><a href="https://newsroom.arm.com/news/iot-reference-design-platform-2024">Arm newsroom: Ethos-U85 + Corstone-320 IoT reference platform</a></p>
 
-<p><strong><a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-n78">Ethos-N78</a></strong> (legacy application-class NPU)</p>
-<blockquote>
-  <p>Second-generation Ethos-N series for immersive edge inference; single-core performance scalable from ~1 to 10+ TOPS via many-core configurations (historical product line still referenced in many SoCs).</p>
-</blockquote>
-
-<p><a href="https://www.anandtech.com/show/12791/arm-details-project-trillium-mlp-architecture">ARM Details "Project Trillium" Machine Learning Processor Architecture</a> (historical)</p>
-
-<p><a name="Synopsys"></a></p>
+<p>
 
+<a name="Synopsys"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Synopsys_logo.png" height="40" loading="lazy" alt="Synopsys" /></div>
-
-<p><strong><a href="https://news.synopsys.com/2022-04-19-Synopsys-Introduces-Industrys-Highest-Performance-Neural-Processor-IP">Synopsys Introduces Industry's Highest Performance Neural Processor IP</a></strong></p>
+<p><strong><a href="https://www.synopsys.com/designware-ip/processor-solutions/neural-processing-unit.html">Synopsys ARC NPX NPU IP</a></strong></p>
 <blockquote>
-  <p>New DesignWare ARC NPX6 NPU IP Delivers Up to 3,500 TOPS Performance for Automotive, Consumer and Data Center Chip Designs</p>
+  <p>2024–2026 — DesignWare ARC NPX neural processor IP family for automotive / consumer / datacenter SoCs, with multi-TOPS scalable configurations and ongoing toolchain updates.</p>
 </blockquote>
 
-<p><a name="Imagination"></a></p>
-
+<a name="Imagination"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Imagination_logo.png" height="60" loading="lazy" alt="Imagination" /></div>
-
-<p><a href="https://www.imaginationtech.com/products/ai/">AI Processors</a></p>
+<p><strong><a href="https://www.imaginationtech.com/products/ai/">Imagination AI / NNA IP</a></strong></p>
 <blockquote>
-  <p>Whether you want smartness residing in the palm of your hand, consumer products or industrial robots, or enabled by powerful servers in the cloud, we can help you achieve your vision. We enable the smartness in your products with our PowerVR Neural Network Accelerators (NNA) and GPUs. Our NC-SDK enables seamless deployment of AI acceleration on either our hardware IP either in isolation or combined. Our NNA provides maximum efficiency with a scalable architecture which enables a wide range of smart edge and end point devices from low performance IoT to high performance RoboTaxi.</p>
+  <p>2024–2026 — PowerVR Neural Network Accelerators (NNA) and GPU+NNA combinations for edge and automotive SoCs, with IMG Continuum software for transformer-class networks.</p>
 </blockquote>
 
-<p><a name="CEVA"></a></p>
-
+<a name="CEVA"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/CEVA_logo.png" height="40" loading="lazy" alt="CEVA" /></div>
-
-<p><a href="https://www.ceva-dsp.com/app/deep-learning/">Deep learning for the real-time embedded world</a></p>
+<p><strong><a href="https://www.ceva-dsp.com/app/deep-learning/">CEVA edge AI / NPU IP</a></strong></p>
 <blockquote>
-  <p>One solution lies in supplying a dedicated low power AI processor for Deep Learning at the edge, combined with a deep neural network (DNN) graph compiler</p>
+  <p>2024–2026 — Low-power AI processor IP and DNN graph compiler for real-time embedded / automotive / IoT inference, including transformer-capable NeuPro-class offerings.</p>
 </blockquote>
 
-<p><a name="Cadence"></a></p>
+<a name="Cadence"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cadence_logo.png" height="40" loading="lazy" alt="Cadence" /></div>
+<p><strong><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Cadence Tensilica AI Platform</a></strong></p>
+<blockquote>
+  <p>2024–2026 — Tensilica NPU / AI DSP IP for on-device and automotive SoCs, with NeuroWeave toolchain updates for modern DNN / transformer workloads.</p>
+</blockquote>
 
-<p><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Tensilica AI Platform</a></p>
-
-<p><a name="VeriSilicon"></a></p>
+<a name="VeriSilicon"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/VeriSilicon_logo.png" height="40" loading="lazy" alt="VeriSilicon" /></div>
-
-<p><a href="https://www.verisilicon.com/en/IPPortfolio/VivanteNPUIP">Vivante® NPU IP</a></p>
+<p><strong><a href="https://www.verisilicon.com/">VeriSilicon VIP / NPU IP &amp; design services</a></strong></p>
 <blockquote>
-  <p>VeriSilicon's Neural Network Processor (NPU) IP is a highly scalable, programmable computer vision and artificial intelligence processor that supports AI operations upgrades for endpoints, edge devices, and cloud devices. Designed to meet a variety of chip sizes and power budgets, the Vivante NPU IP is a cost-effective, high-quality neural network acceleration engine solution.</p>
+  <p>2024–2026 — Acuity / VIP NPU IP and turnkey design services widely used in China and global edge SoCs for vision and on-device GenAI.</p>
 </blockquote>
 
 <div align="center" class="section-title"><h2><a name="Startups"></a>IV. Startups</h2></div>
 
 <hr class="section-rule" />
 
-<p><a name="Cerebras"></a></p>
+<a name="Cerebras"></a>
 
 <div align="center" class="vendor-logo"><a href="https://www.cerebras.net/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cerebras_logo.png" height="50" loading="lazy" alt="Cerebras" /></a></div>
 
@@ -547,32 +369,7 @@
   <p>March 2024 — WSE-3 (5nm) powers the CS-3 system with ~900,000 AI-optimized cores and 125 petaflops peak AI performance, doubling WSE-2 performance at the same power/price point for large-model training and high-throughput inference.</p>
 </blockquote>
 
-<p><strong><a href="https://www.cerebras.net/press-release/cerebras-unveils-andromeda-a-13.5-million-core-ai-supercomputer-that-delivers-near-perfect-linear-scaling-for-large-language-models">Cerebras Unveils Andromeda, a 13.5 Million Core AI Supercomputer that Delivers Near-Perfect Linear Scaling for Large Language Models</a></strong></p>
-<blockquote>
-  <p>Delivering more than 1 Exaflop of AI compute and 120 Petaflops of dense compute, Andromeda is one of the largest AI supercomputers ever built, and is dead simple to use</p>
-</blockquote> 
-
-<p><a href="https://www.cerebras.net/blog/cerebras-sets-record-for-largest-ai-models-ever-trained-on-single-device">Cerebras Sets Record for Largest AI Models Ever Trained on Single Device</a></p>
-<blockquote>
-  <p>We are announcing the largest models ever trained on a single device. Using the Cerebras Software Platform (CSoft), our customers can easily train state-of-the-art GPT language models (such as GPT-3[i] and GPT-J[ii]) with up to 20 billion parameters on a single CS-2 system. Running on a single CS-2, these models take minutes to set up and users can quickly move between models with just a few keystrokes. With clusters of GPUs, this takes months of engineering work.</p>
-</blockquote> 
- 
-<p><a href="https://www.anandtech.com/show/17061/cerebras-completes-series-f-funding-another-250m-for-4b-valuation">Cerebras Completes Series F Funding, Another $250M for $4B Valuation</a></p>
-<blockquote>
-  <p>The new Series F funding round nets the company another $250m in capital, bringing the total raised through venture capital up to $720 million.</p>
-</blockquote> 
- 
-<p><a href="https://www.anandtech.com/show/16626/cerebras-unveils-wafer-scale-engine-two-wse2-26-trillion-transistors-100-yield">Cerebras Unveils Wafer Scale Engine Two (WSE2): 2.6 Trillion Transistors, 100% Yield</a></p>
-<blockquote>
-  <p>Two years ago Cerebras unveiled a revolution in silicon design: a processor as big as your head, using as much area on a 12-inch wafer as a rectangular design would allow, built on 16nm, focused on both AI as well as HPC workloads. Today the company is launching its second generation product, built on TSMC 7nm, with more than double the cores and more than double of everything.</p>
-</blockquote>
-
-<p><a href="https://techcrunch.com/2019/11/19/the-cerebras-cs-1-computes-deep-learning-ai-problems-by-being-bigger-bigger-and-bigger-than-any-other-chip/">The Cerebras CS-1 computes deep learning AI problems by being bigger, bigger, and bigger than any other chip</a></p>
-<blockquote>
-  <p>Today, the company announced the launch of its end-user compute product, the Cerebras CS-1, and also announced its first customer of Argonne National Laboratory.</p>
-</blockquote>
-
-<p><a name="Graphcore"></a></p>
+<a name="Graphcore"></a>
 
 <div align="center" class="vendor-logo"><a href="https://www.graphcore.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Graphcore_logo.png" height="70" loading="lazy" alt="Graphcore" /></a></div>
 
@@ -581,65 +378,25 @@
   <p>Jul 2024 — SoftBank acquired Graphcore as a wholly owned subsidiary (terms undisclosed; press estimates ~$500M). Graphcore continues under its own brand, with SoftBank backing next-generation IPU / AI compute development (including SoftBank-ecosystem follow-on investment reporting into 2026).</p>
 </blockquote>
 
-<p><a href="https://www.reuters.com/technology/artificial-intelligence/japans-softbank-acquires-british-ai-chipmaker-graphcore-2024-07-11/">Reuters: SoftBank acquires Graphcore</a></p>
+<p>
 
-<p><strong><a href="https://www.eetimes.com/graphcore-supercharges-ipu-with-wafer-on-wafer/">Graphcore Supercharges IPU with Wafer-on-Wafer</a></strong> (historical)</p>
-<blockquote>
-  <p>Graphcore unveiled its third-generation intelligence processing unit (IPU), the first processor to be built using 3D wafer-on-wafer (WoW) technology.</p>
-</blockquote>
- 
-<p><a href="https://www.graphcore.ai/mk2-benchmarks">MK2 PERFORMANCE BENCHMARKS</a></p>
+<p>
 
-<p><a href="https://techcrunch.com/2020/02/24/graphcore-the-ai-chipmaker-raises-another-150m-at-a-1-95b-valuation/">Graphcore, the AI chipmaker, raises another $150M at a $1.95B valuation</a></p>
+<a name="Tenstorrent"></a>
+<div align="center" class="vendor-logo"><a href="http://tenstorrent.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tenstorrent_logo.png" height="80" loading="lazy" alt="Tenstorrent" /></a></div>
+<p><strong><a href="https://tenstorrent.com/">Tenstorrent Wormhole / Blackhole</a></strong></p>
 <blockquote>
-  <p>Graphcore, the Bristol-based startup that designs processors specifically for artificial intelligence applications, announced it has raised another $150 million in funding for R&D and to continue bringing on new customers. It’s valuation is now $1.95 billion.</p>
+  <p>2024–2026 — Tensix-based AI processors (Wormhole shipping; Blackhole next) with open software (TT-Metalium) and growing rack-scale deployments; Jim Keller–led roadmap emphasizes scalable chiplet meshes.</p>
 </blockquote>
 
-<p><a href="https://mp.weixin.qq.com/s/CH9h8dUtoNK_2ZfkK5YU0g">解密又一个xPU：Graphcore的IPU</a> give some analysis on its IPU architecture.</p>
-<p><a href="https://mp.weixin.qq.com/s/AMuqeaShqEv3DnibH3scEA">Graphcore AI芯片：更多分析</a> More analysis.</p>
-<p><a href="https://mp.weixin.qq.com/s/qP0zsSA7SQWXDqWGEAXmOg">深度剖析AI芯片初创公司Graphcore的IPU</a> In-depth analysis after more information was disclosed.</p>
-
-<p><a name="Tenstorrent"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://tenstorrent.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tenstorrent_logo.png" height="100" loading="lazy" alt="Tenstorrent" /></a></div>
-
-<p><strong><a href="https://tenstorrent.com/">Tenstorrent Blackhole / Wormhole AI processors</a></strong></p>
-<blockquote>
-  <p>Tenstorrent builds RISC-V based AI computers (Wormhole, Blackhole) with an open software stack, targeting datacenter and edge deployments under Jim Keller’s architecture leadership.</p>
-</blockquote>
-
-<p><a href="https://www.prnewswire.com/news-releases/tenstorrent-raises-over-200-million-at-1-billion-valuation-to-create-programmable-high-performance-ai-computers-301295913.html">Tenstorrent Raises over $200 million at $1 billion Valuation to Create Programmable, High Performance AI Computers</a></p>
-<blockquote>
-  <p>TORONTO, May 20, 2021 /PRNewswire/ - Tenstorrent, a hardware start-up developing next generation computers, announced today that it has raised over $200 million in a recent funding round that values the company at $1 billion. The round was led by Fidelity Management and Research Company and includes additional investments from Eclipse Ventures, Epic CG and Moore Capital. </p>
-</blockquote> 
- 
-<p><a href="https://www.anandtech.com/show/16709/an-interview-with-tenstorrent-ceo-ljubisa-bajic-and-cto-jim-keller">An Interview with Tenstorrent: CEO Ljubisa Bajic and CTO Jim Keller</a></p>
- 
-
-<p><a name="Blaize"></a></p>
-
+<a name="Blaize"></a>
 <div align="center" class="vendor-logo"><a href="https://www.blaize.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blaize_logo.png" height="40" loading="lazy" alt="Blaize" /></a></div>
-
-<p><a href="https://www.eetimes.com/automotive-ai-startup-blaize-closes-71-million-funding-round/">Automotive AI Startup Blaize Closes $71 Million Funding Round</a></p>
+<p><strong><a href="https://www.blaize.com/">Blaize AI edge platforms (Nasdaq: BZAI)</a></strong></p>
 <blockquote>
-  <p>Blaize, formerly ThinCI, has closed a Series D round of funding at $71 million. New investor Franklin Templeton and existing investor Temasek led the round, along with participation from Denso and other new and existing investors. This round brings Blaize’s total funding to around $155 million total.</p>
+  <p>2025 — Listed via SPAC; Graph Streaming Processor (GSP) architecture for edge AI vision / GenAI inference in industrial and smart-city deployments.</p>
 </blockquote>
 
-<p><a name="Koniku"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://koniku.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Koniku_logo.png" height="50" loading="lazy" alt="Koniku" /></a></div>
-
-<blockquote>
-  <p>Founded in 2014, Newark, California startup <a href="http://koniku.io/">Koniku</a> has taken in $1.65 million in funding so far to become “the world’s first neurocomputation company“. The idea is that since the brain is the most powerful computer ever devised, why not reverse engineer it? Simple, right? Koniku is actually integrating biological neurons onto chips and has made enough progress that they claim to have AstraZeneca as a customer. Boeing has also signed on with a letter of intent to use the technology in chemical-detecting drones.</p>
-</blockquote>
-
-<p><a name="Adapteva"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://www.adapteva.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Adapteva_logo.png" height="70" loading="lazy" alt="Adapteva" /></a></div>
-
-<p><a href="http://www.adapteva.com/">Adapteva</a> has taken in $5.1 million in funding from investors that include mobile giant Ericsson. <a href="http://www.parallella.org/docs/e5_1024core_soc.pdf">The paper "Epiphany-V: A 1024 processor 64-bit RISC System-On-Chip"</a> describes the design of Adapteva's 1024-core processor chip in 16nm FinFet technology. </p>
-
-<p><a name="Mythic"></a></p>
+<a name="Mythic"></a>
 
 <div align="center" class="vendor-logo"><a href="https://mythic.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Mythic_logo.png" height="20" loading="lazy" alt="Mythic" /></a></div>
 
@@ -647,34 +404,22 @@
 <blockquote>
   <p>Dec 2025 — Oversubscribed round led by DCVC (strategic participation including Honda and Lockheed Martin) to scale Mythic’s Analog Processing Units (APUs) — flash-based compute-in-memory for energy-efficient inference at the edge and in data centers. Roadmap emphasizes chiplet-based next-gen APUs for robotics, automotive, and defense.</p>
 </blockquote>
-
 <p><strong><a href="https://mythic.ai/whats-new/mythic-acquires-videantis-one-of-europes-leading-digital-processor-ip-companies-to-build-the-worlds-most-energy-efficient-ai-compute-platform/">Mythic acquires Videantis for hybrid analog/digital AI platform</a></strong></p>
 <blockquote>
   <p>May 2026 — Acquisition of Videantis GmbH adds a production digital processor IP + software stack alongside Mythic’s analog in-memory compute, targeting a hybrid platform with large claimed efficiency gains vs. conventional GPU inference.</p>
 </blockquote>
 
-<p><a href="https://mythic.ai/products/m1076-analog-matrix-processor/">M1076 Analog Matrix Processor</a> (shipping product context) ·
-<a href="https://venturebeat.com/2021/06/07/mythic-launches-analog-ai-processor-that-consumes-10-times-less-power/">VentureBeat: M1076 launch</a> (historical)</p>
+<p>
 
-<p><a name="Brainchip"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://www.brainchipinc.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Brainchip_logo.png" height="40" loading="lazy" alt="BrainChip" /></a></div>
-
-<p><a href="https://venturebeat.com/2022/01/18/brainchip-launches-neuromorphic-process-for-ai-at-the-edge/">BrainChip launches neuromorphic process for AI at the edge</a> </p>
-<blockquote>
-  <p>BrainChip today announced the commercialization of its Akida neural networking processor. Aimed at a variety of edge and internet of things (IoT) applications, BrainChip claims to be the first commercial producer of neuromorphic AI chips, which could deliver benefits in ultra-low power and performance over conventional approaches.</p>
-</blockquote>
-
-<p><a name="Deepvision"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://deepvision.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Deepvision_logo.png" height="40" loading="lazy" alt="Deep Vision" /></a></div>
-
-<p><a href="https://www.businesswire.com/news/home/20210928005270/en/AI-Processor-Chipmaker-Deep-Vision-Raises-35-Million-in-Series-B-Funding">AI Processor Chipmaker Deep Vision Raises $35 Million in Series B Funding</a></p>
+<a name="Kinara"></a>
+<div align="center" class="vendor-logo"><a href="https://www.kinara.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Deepvision_logo.png" height="40" loading="lazy" alt="Kinara" /></a></div>
+<div align="center" class="vendor-title"><h3>Kinara (ex–Deep Vision)</h3></div>
+<p><strong><a href="https://www.nxp.com/company/about-nxp/newsroom/NW-NXP-TO-ACQUIRE-KINARA">NXP to acquire Kinara (2025)</a></strong></p>
 <blockquote>
-  <p>Tiger Global Leads Series B Financing, Enabling Deep Vision to Expand Video Analytics and Natural Language Processing Capabilities in Edge Computing Applications</p>
+  <p>Feb 2025 — NXP announced acquisition of Kinara (rebranded from Deep Vision) for edge AI / Ara / Prisma-class NPUs used in vision and on-device GenAI inference; expands NXP’s industrial and automotive edge AI portfolio.</p>
 </blockquote>
 
-<p><a name="Groq"></a></p>
+<a name="Groq"></a>
 
 <div align="center" class="vendor-title"><h3><a href="https://groq.com/">Groq</a></h3></div>
 
@@ -683,33 +428,7 @@
   <p>In late 2025 NVIDIA licensed Groq’s LPU technology and brought on key engineering talent. At GTC 2026, NVIDIA described integrating Groq-class LPU engines into the Vera Rubin AI factory stack for low-latency / high-efficiency token generation alongside Rubin GPUs.</p>
 </blockquote>
 
-<p><strong><a href="https://www.eetimes.com/groq-demos-fast-llms-on-4-year-old-silicon/">Groq Demonstrates Fast LLMs on 4-Year-Old Silicon</a></strong></p>
-<blockquote>
-  <p>MOUNTAIN VIEW, CALIF. — Groq has repositioned its first-generation AI inference chip as a language processing unit (LPU), and demonstrated Meta’s Llama-2 70-billion–parameter large language model (LLM) running inference at 240 tokens per second per user. Groq CEO Jonathan Ross told EE Times that the company had Llama-2 up and running on the company’s 10-rack (64-chip) cloud-based dev system in “a couple of days.” This system is based on the company’s first gen AI silicon, released four years ago.</p>
-</blockquote>
-
-<p><a href="https://www.forbes.com/sites/amyfeldman/2021/04/14/ai-chip-startup-groq-founded-by-ex-googlers-raises-300-million-to-power-autonomous-vehicles-and-data-centers/">AI Chip Startup Groq, Founded By Ex-Googlers, Raises $300 Million To Power Autonomous Vehicles And Data Centers</a></p>
-<blockquote>
-  <p>Jonathan Ross left Google to launch next-generation semiconductor startup Groq in 2016. Today, the Mountain View, California-based firm said that it had raised $300 million led by Tiger Global Management and billionaire investor Dan Sundheim’s D1 Capital as it officially launched into public view. </p>
-</blockquote>
-
-<p><a name="Kneron"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://www.kneron.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Kneron_logo.png" height="60" loading="lazy" alt="Kneron" /></a></div>
-
-<p><a href="https://www.prnewswire.com/news-releases/kneron-to-accelerate-edge-ai-development-with-more-than-10-million-usd-series-a-financing-300556674.html">Kneron to Accelerate Edge AI Development with more than 10 Million USD Series A Financing</a></p>
-
-<p><a name="GTI"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.gyrfalcontech.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GTI_Logo.png" height="40" loading="lazy" alt="Gyrfalcon" /></a></div>
-
-<p>According to this article, <a href="https://www.prnewswire.com/news-releases/gyrfalcon-offers-automotive-ai-chip-technology-300860069.html">"Gyrfalcon offers Automotive AI Chip Technology"</a></p>
-
-<blockquote>
-  <p>Gyrfalcon Technology Inc. (GTI), has been promoting matrix-based application specific chips for all forms of AI since offering their production versions of AI accelerator chips in September 2017. Through the licensing of its proprietary technology, the company is confident it can help automakers bring highly competitive AI chips to production for use in vehicles within 18 months, along with significant gains in AI performance, improvements in power dissipation and cost advantages.</p>
-</blockquote>
-
-<p><a name="SambaNova"></a></p>
+<a name="SambaNova"></a>
 
 <div align="center" class="vendor-logo"><a href="https://sambanova.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SambaNova_logo.png" height="40" loading="lazy" alt="SambaNova" /></a></div>
 
@@ -718,44 +437,17 @@
   <p>Feb 2026 — Fifth-generation Reconfigurable Dataflow Unit (SN50) targets large-scale agentic inference (vendor claims up to ~5× compute and ~4× network bandwidth vs. SN40L; scales toward 256 accelerators). SoftBank named as an early deploy partner; shipping targeted for H2 2026. Planned multi-year collaboration with Intel for heterogeneous inference stacks; oversubscribed Series E led with Vista / Cambium participation including Intel Capital.</p>
 </blockquote>
 
-<p><a href="https://sambanova.ai/blog/introducing-the-sn50-rdu-purpose-built-for-agentic-inference">Introducing the SN50 RDU</a> ·
-<a href="https://sambanova.ai/products/rdu-ai-chips">RDU product page</a></p>
-
-<p><strong><a href="https://venturebeat.com/ai/sambanova-unveils-new-ai-chip-to-power-full-stack-ai-platform/">SN40L RDU powers SambaNova Suite</a></strong> (prior gen)</p>
-<blockquote>
-  <p>Fourth-generation SN40L RDU with three-tier memory (SRAM / HBM / DRAM) for full-stack LLM platforms — still the primary shipping generation ahead of SN50.</p>
-</blockquote>
-
-<p><a href="https://techcrunch.com/2021/04/13/sambanova-raises-676m-at-a-5-1b-valuation-to-double-down-on-cloud-based-ai-software-for-enterprises/">Series D funding context</a> (historical) ·
-<a href="https://sambanova.ai/articles/introducing-sambanova-systems-datascale-a-new-era-of-computing/">DataScale systems</a></p>
-
-<p><a name="GreenWaves"></a></p>
-<div align="center" class="vendor-logo"><a href="https://greenwaves-technologies.com/en/greenwaves-technologies-2/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GreenWaves_logo.png" height="50" loading="lazy" alt="GreenWaves" /></a></div>
-
-<p><a href="https://www.eetimes.eu/greenwaves-shows-off-advanced-audio-demos/">GreenWaves Shows Off Advanced Audio Demos</a></p>
-<blockquote>
-  <p>The Gap9 processor, a successor to Gap8 which targets computer vision in IoT devices, is an ultra-low power neural network processor suitable for battery-powered devices. GreenWaves’ vice president of marketing Martin Croome told EE Times Europe that the company decided to focus Gap9 on the hearables market after receiving traction from this sector for Gap8.</p>
-</blockquote>
-
-<p><a name="Lightelligence"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.lightelligence.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightelligence_logo.png" height="60" loading="lazy" alt="Lightelligence" /></a></div>
+<p>
 
-<p><strong><a href="https://www.eetimes.com/optical-computing-chip-runs-hardest-math-problems-100x-faster-than-gpus/">Optical Chip Solves Hardest Math Problems Faster than GPUs</a></strong></p>
+<a name="SynSense"></a>
+<div align="center" class="vendor-logo"><a href="https://www.synsense.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/aiCTX_logo.png" height="40" loading="lazy" alt="SynSense" /></a></div>
+<div align="center" class="vendor-title"><h3>SynSense (ex–aiCTX)</h3></div>
+<p><strong><a href="https://www.synsense.ai/">SynSense neuromorphic / event-based sensing+compute</a></strong></p>
 <blockquote>
-  <p>Optical computing startup Lightelligence has demonstrated a silicon photonics accelerator running the Ising problem more than 100 times faster than a typical GPU setup.</p>
+  <p>2024–2026 — Continues Speck / Xylo-class neuromorphic chips; 2024 iniVation collaboration and subsequent funding extend event-based vision + edge AI offerings.</p>
 </blockquote>
 
-<p><a name="Lightmatter"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.lightmatter.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Lightmatter_logo.png" height="50" loading="lazy" alt="Lightmatter" /></a></div>
-
-<p><a href="https://www.eetimes.com/lightmatter-raises-more-funding-for-photonic-ai-chip/">Lightmatter Raises More Funding for Photonic AI Chip</a></p>
-<blockquote>
-  <p>ightmatter, the MIT spinout building AI accelerators with a silicon photonics computing engine, announced a Series B funding round, raising an additional $80 million. The company’s technology is based on proprietary silicon photonics technology which manipulates coherent light inside a chip to perform calculations very quickly while using very little power</p>
-</blockquote>
-
-<p><a name="Hailo"></a></p>
+<a name="Hailo"></a>
 
 <div align="center" class="vendor-logo"><a href="https://hailo.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Hailo_logo.png" height="60" loading="lazy" alt="Hailo" /></a></div>
 
@@ -764,181 +456,23 @@
   <p>Jul 2025 — Second-generation Hailo-10H brings on-device LLM / VLM / GenAI capabilities to the edge (~40 TOPS INT4 class, ~2.5W typical), while retaining vision workloads from the Hailo-8 generation. Automotive-qualified (AEC-Q100 Grade 2) with 2026 SOP targeting for some designs.</p>
 </blockquote>
 
-<p><a href="https://hailo.ai/products/ai-accelerators/hailo-10h-ai-accelerator/">Hailo-10H product page</a> ·
-<a href="https://hailo.ai/products/ai-accelerators/hailo-8-ai-accelerator/">Hailo-8</a></p>
-
-<p><a href="https://www.eetimes.com/unicorn-ai-chipmaker-hailo-raises-136-million/">‘Unicorn’ AI Chipmaker Hailo Raises $136 Million</a> (historical)</p>
-<blockquote>
-  <p>Israeli AI chip startup Hailo has raised $136 million in a Series C funding round, bringing the company’s total to $224 million. The company has also reportedly reached “unicorn” status.</p>
-</blockquote>
-
-<p><a name="Tachyum"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://www.tachyum.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tachyum_logo.png" height="40" loading="lazy" alt="Tachyum" /></a></div>
-
-<p><a href="https://www.hpcwire.com/off-the-wire/tachyum-launches-prodigy-universal-processor/">Tachyum Launches Prodigy Universal Processor</a></p>
-<blockquote>
-  <p>May 11, 2021 — Tachyum today launched the world’s first universal processor, Prodigy, which unifies the functionality of a CPU, GPU and TPU in a single processor, creating a homogeneous architecture, while delivering massive performance improvements at a cost many times less than competing products</p>
-</blockquote>
-
-<p><a name="Alphaics"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.alphaics.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Alphaics_logo.png" height="50" loading="lazy" alt="AlphaICs" /></a></div>
-
-<p><a href="https://www.eetimes.com/alphaics-begins-sampling-its-deep-learning-co-processor/">AlphaICs Begins Sampling Its Deep Learning Co-Processor</a></p>
-<blockquote>
-  <p>AlphaICs, a startup developing edge AI and learning silicon aimed at smart vision applications, is sampling its deep learning co-processor, Gluon, that also comes with a software development kit.</p>
-</blockquote>
-
-<p><a name="Syntiant"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.syntiant.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Syntiant_logo.png" height="30" loading="lazy" alt="Syntiant" /></a></div>
-
-<p><a href="https://semiengineering.com/syntiant-analog-deep-learning-chips/">Syntiant: Analog Deep Learning Chips</a></p>
-
-<blockquote>
-  <p>Startup Syntiant Corp. is an Irvine, Calif. semiconductor company led by former top Broadcom engineers with experience in both innovative design and in producing chips designed to be produced in the billions, according to company CEO Kurt Busch.</p>
-</blockquote>
-
-<p><a name="aiCTX"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://aictx.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/aiCTX_logo.png" height="40" loading="lazy" alt="aiCTX" /></a></div>
-
-<p><strong><a href="https://www.eetimes.com/document.asp?doc_id=1333983">Baidu Backs Neuromorphic IC Developer</a></strong></p>
-
-<blockquote>
-  <p>MUNICH — Swiss startup aiCTX has closed a $1.5 million pre-A funding round from Baidu Ventures to develop commercial applications for its low-power neuromorphic computing and processor designs and enable what it calls “neuromorphic intelligence.” It is targeting low-power edge-computing embedded sensory processing systems.</p>
-</blockquote>
-
-<p><a name="Flexlogix"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://www.flex-logix.com/nmax"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/flexlogix_logo.png" height="40" loading="lazy" alt="Flex Logix" /></a></div>
-
-<p><strong><a href="https://www.zdnet.com/article/flex-logix-has-two-paths-to-making-a-lot-of-money-challenging-nvidia-in-ai/">Flex Logix has two paths to making a lot of money challenging Nvidia in AI</a></strong></p>
-
-<blockquote>
-  <p>The programmable chip company scores $55 million in venture backing, bringing its total haul to $82 million</p>
-</blockquote>
-
-<p><a name="PFN"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://projects.preferred.jp/mn-core/en/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/PFN_logo.png" height="40" loading="lazy" alt="Preferred Networks" /></a></div>
-
-<p><strong><a href="https://www.preferred-networks.jp/en/news">Preferred Networks develops a custom deep learning processor MN-Core for use in MN-3, a new large-scale cluster, in spring 2020</a></strong></p>
-
-<blockquote>
-  <p>Dec. 12, 2018, Tokyo Japan – Preferred Networks, Inc. (“PFN”, Head Office: Tokyo, President & CEO: Toru Nishikawa) announces that it is developing MN-Core (TM), a processor dedicated to deep learning and will exhibit this independently developed hardware for deep learning, including the MN-Core chip, board, and server, at the SEMICON Japan 2018, held at Tokyo Big Site. 
- </p>
-</blockquote>
-
-<p><a name="Cornami"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://cornami.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cornami_logo.jpg" height="30" loading="lazy" alt="Cornami" /></a></div>
-
-<p><strong><a href="https://www.zdnet.com/article/ai-startup-cornami-reveals-details-of-neural-net-chip/">AI Startup Cornami reveals details of neural net chip</a></strong></p>
-
-<blockquote>
-  <p>Stealth startup Cornami on Thursday revealed some details of its novel approach to chip design to run neural networks. CTO Paul Masters says the chip will finally realize the best aspects of a technology first seen in the 1970s. 
- </p>
-</blockquote>
-
-<p><a name="Anaflash"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://anaflash.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Anaflash_logo.png" height="40" loading="lazy" alt="Anaflash" /></a></div>
-
-<p><strong><a href="https://www.smart2zero.com/news/ai-chip-startup-offers-new-edge-computing-solution">AI chip startup offers new edge computing solution</a></strong></p>
-
-<blockquote>
-  <p>Anaflash Inc. (San Jose, CA) is a startup company that has developed a test chip to demonstrate analog neurocomputing taking place inside logic-compatible embedded flash memory. 
- </p>
-</blockquote>
-
-<p><a name="Optalysys"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.optalysys.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Optalysys_logo.png" height="40" loading="lazy" alt="Optalysys" /></a></div>
-
-<p><strong><a href="https://www.globenewswire.com/news-release/2019/03/07/1749510/0/en/Optalysys-launches-world-s-first-commercial-optical-processing-system-the-FT-X-2000.html">Optalysys launches world’s first commercial optical processing system, the FT:X 2000</a></strong></p>
-
-<blockquote>
-  <p>Optalysys develops Optical Co-processing technology which enables new levels of processing capability delivered with a vastly reduced energy consumption compared with conventional computers. Its first coprocessor is based on an established diffractive optical approach that uses the photons of low-power laser light instead of conventional electricity and its electrons. This inherently parallel technology is highly scalable and is the new paradigm of computing. 
- </p>
-</blockquote>
-
-<p><a name="etacompute"></a></p>
+<p>
 
-<div align="center" class="vendor-logo"><a href="https://etacompute.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/etacompute_logo.png" height="80" loading="lazy" alt="Eta Compute" /></a></div>
+<p>
 
-<p><a href="https://spectrum.ieee.org/tech-talk/semiconductors/processors/lowpower-ai-startup-eta-compute-delivers-first-commercial-chips">Low-Power AI Startup Eta Compute Delivers First Commercial Chips</a></p>
-<blockquote>
-  <p>The firm pivoted away from riskier spiking neural networks using a new power management scheme</p>
-</blockquote>
+<p>
 
-<p><a href="https://spectrum.ieee.org/tech-talk/semiconductors/processors/eta-compute-debuts-spiking-neural-network-chip-for-edge-ai">Eta Compute Debuts Spiking Neural Network Chip for Edge AI</a></p>
-<blockquote>
-  <p>Chip can learn on its own and inference at 100-microwatt scale, says company at Arm TechCon.</p>
-</blockquote>
-
-<p><a name="Achronix"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.achronix.com/product/speedster7t/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Achronix_logo.png" height="30" loading="lazy" alt="Achronix" /></a></div>
-
-<p><strong><a href="https://www.eetimes.com/document.asp?doc_id=1334717">Achronix Rolls 7-nm FPGAs for AI</a></strong></p>
-
-<blockquote>
-  <p>Achronix is back in the game of providing full-fledged FPGAs with a new high-end 7-nm family, joining the Gold Rush of silicon to accelerate deep learning. It aims to leverage novel design of its AI block, a new on-chip network, and use of GDDR6 memory to provide similar performance at a lower cost than larger rivals Intel and Xilinx.</p>
-</blockquote>
-
-<p><a name="Areanna"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://areanna-ai.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Areanna_logo.png" height="60" loading="lazy" alt="Areanna" /></a></div>
-
-<p><strong><a href="https://www.eetimes.com/document.asp?doc_id=1334947#">Startup Runs AI in Novel SRAM</a></strong></p>
-
-<blockquote>
-  <p>Areanna is the latest example of an explosion of new architectures spawned by the rise of deep learning. The debut of a whole new approach to computing has fired imaginations of engineers around the industry hoping to be the next Hewlett and Packard.</p>
-</blockquote>
-
-<p><a name="Neuroblade"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.neuroblade.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neuroblade_logo.png" height="120" loading="lazy" alt="NeuroBlade" /></a></div>
-
-<p><strong><a href="https://www.eetasia.com/news/article/NeuroBlade-Preps-Inference-Chip">NeuroBlade Preps Inference Chip</a></strong></p>
-
-<blockquote>
-  <p>Add NeuroBlade to the dozens of startups working on AI silicon. The Israeli company just closed a $23 million Series A, led by the founder of Check Point Software and with participation from Intel Capital.</p>
-</blockquote>
-
-<p><a name="Luminous"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.luminouscomputing.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Luminous_logo.png" height="90" loading="lazy" alt="Luminous" /></a></div>
-
-<p><strong><a href="https://www.technologyreview.com/s/613668/ai-chips-uses-optical-semiconductor-machine-learning/">Bill Gates just backed a chip startup that uses light to turbocharge AI</a></strong></p>
-
-<blockquote>
-  <p>Luminous Computing has developed an optical microchip that runs AI models much faster than other semiconductors while using less power.</p>
-</blockquote>
-
-<p><a name="Efinix"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.efinixinc.com"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Efinix_logo.png" height="25" loading="lazy" alt="Efinix" /></a></div>
-
-<p><strong><a href="https://www.zdnet.com/article/chip-startup-efinix-hopes-to-bootstrap-ai-efforts-in-iot/">Chip startup Efinix hopes to bootstrap AI efforts in IoT</a></strong></p>
-
-<blockquote>
-  <p>Six-year-old startup Efinix has created an intriguing twist on the FPGA technology dominated by Intel and Xiliinx; the company hopes its energy-efficient chips will bootstrap the market for embedded AI in the Internet of Things.</p>
-</blockquote>
+<p>
 
-<p><a name="AIstorm"></a></p>
+<p>
 
-<div align="center" class="vendor-logo"><a href="https://aistorm.ai"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AIstorm_logo.png" height="60" loading="lazy" alt="AIstorm" /></a></div>
+<p>
 
-<p><strong><a href="https://venturebeat.com/2019/02/11/aistorm-raises-13-2-million-for-ai-edge-computing-chips/">AIStorm raises $13.2 million for AI edge computing chips</a></strong></p>
+<p>
 
-<blockquote>
-  <p>David Schie, a former senior executive at Maxim, Micrel, and Semtech, thinks both markets are ripe for disruption. He — along with WSI, Toshiba, and Arm veterans Robert Barker, Andreas Sibrai, and Cesar Matias — in 2011 cofounded AIStorm, a San Jose-based artificial intelligence (AI) startup that develops chipsets that can directly process data from wearables, handsets, automotive devices, smart speakers, and other internet of things (IoT) devices. </p>
-</blockquote>
+<p>
 
-<p><a name="SiMa"></a></p>
+<a name="SiMa"></a>
 
 <div align="center" class="vendor-logo"><a href="https://sima.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SiMa_logo.png" height="40" loading="lazy" alt="SiMa.ai" /></a></div>
 
@@ -947,79 +481,9 @@
   <p>Aug 2025 — Second-generation MLSoC Modalix ships for edge / Physical AI (robots, industrial, automotive, vision), targeting CNNs + Transformers/LLMs/GenAI under ~10W, with SoM/devkits and the LLiMa framework for on-device LLM deployment.</p>
 </blockquote>
 
-<p><a href="https://sima.ai/press-release/sima-ai-expands-one-platform-for-edge-ai-with-mlsoc-modalix/">MLSoC Modalix product family announcement</a> (2024) ·
-<a href="https://sima.ai/press-release/sima-ai-launches-modalix-pcie-card-for-industrial-edge-ai-llms/">Modalix PCIe card for industrial edge LLMs</a> (2026)</p>
-
-<p><a href="https://www.businesswire.com/news/home/20200512005313/en/SiMa.ai-Raises-30-Million-Series-Investment-Led">SiMa.ai Raises $30 Million in Series A</a> (historical)</p>
-<blockquote>
-  <p>SAN JOSE, Calif.--(BUSINESS WIRE)--SiMa.ai, the company enabling high performance machine learning to go green, today announced its Machine Learning SoC (MLSoC) platform – the industry’s first unified solution to support traditional compute with high performance, lowest power, safe and secure machine learning inference. Delivering the highest frames per second per watt, SiMa.ai’s MLSoC is the first machine learning platform to break the 1000 FPS/W barrier for ResNet-501. In customer engagements, the company has demonstrated 10-30x improvement in FPS/W through its automated software flow across a wide range of embedded edge applications, over today’s competing solutions. The platform will provide machine learning solutions that range from 50 TOPs@5W to 200 TOPs@20W, delivering an industry first of 10 TOPs/W for high performance inference.</p>
-</blockquote>
-
-<p><a href="https://www.businesswire.com/news/home/20191022005079/en/SiMa.ai%E2%84%A2-Introduces-MLSoC%E2%84%A2">SiMa.ai™ Introduces MLSoC™ – First Machine Learning Platform to Break 1000 FPS/W Barrier with 10-30x Improvement over Alternative Solutions</a></p>
-<blockquote>
-  <p>SiMa.ai, the company enabling high performance machine learning to go green, today announced its Machine Learning SoC (MLSoC) platform – the industry’s first unified solution to support traditional compute with high performance, lowest power, safe and secure machine learning inference. Delivering the highest frames per second per watt, SiMa.ai’s MLSoC is the first machine learning platform to break the 1000 FPS/W barrier for ResNet-501. In customer engagements, the company has demonstrated 10-30x improvement in FPS/W through its automated software flow across a wide range of embedded edge applications, over today’s competing solutions. The platform will provide machine learning solutions that range from 50 TOPs@5W to 200 TOPs@20W, delivering an industry first of 10 TOPs/W for high performance inference.</p>
-</blockquote>
-
-<p><a name="Untether"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://untether.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Untether_logo.png" height="40" loading="lazy" alt="Untether AI" /></a></div>
-
-<p><strong><a href="https://venturebeat.com/2021/07/20/untether-ai-nabs-125m-for-ai-acceleration-chips/">Untether AI nabs $125M for AI acceleration chips</a></strong></p>
-<blockquote>
-  <p>Untether AI, a startup developing custom-built chips for AI inferencing workloads, today announced it has raised $125 million from Tracker Capital Management and Intel Capital. The round, which was oversubscribed and included participation from Canada Pension Plan Investment Board and Radical Ventures, will be used to support customer expansion.</p>
-</blockquote>
-
-<p><a name="GrAI"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.graimatterlabs.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/GrAI_logo.png" height="40" loading="lazy" alt="GrAI Matter Labs" /></a></div>
-
-<p><strong><a href="https://venturebeat.com/2019/09/18/grai-matter-labs-reveals-neuronflow-technology-and-announces-graiflow-sdk/">GrAI Matter Labs Reveals NeuronFlow Technology and Announces GrAIFlow SDK</a></strong></p>
-
-<blockquote>
-  <p>GrAI Matter Labs (aka GML), a neuromorphic computing pioneer today revealed NeuronFlow – a new programmable processor technology – and announced an early access program to its GrAIFlow software development kit.</p>
-</blockquote>
-
-<p><a name="Rain"></a></p>
+<p>
 
-<div align="center" class="vendor-logo"><a href="http://rain-neuromorphics.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Rain_logo.png" height="40" loading="lazy" alt="Rain" /></a></div>
-
-<p><strong><a href="https://www.crunchbase.com/organization/rain-neuromorphics">Rain Neuromorphics on Crunchbase</a></strong></p>
-<blockquote>
-  <p>We build artificial intelligence processors, inspired by the brain. Our mission is to enable brain-scale intelligence.</p>
-</blockquote>
-
-<p><a name="ABR"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://appliedbrainresearch.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ABR_logo.png" height="40" loading="lazy" alt="Applied Brain Research" /></a></div>
-
-<p><strong><a href="https://www.crunchbase.com/organization/applied-brain-research">Applied Brain Research on Crunchbase</a></strong></p>
-<blockquote>
-  <p>ABR makes the world's most advanced neuromoprhic compiler, runtime and libraries for the emerging space of neuromorphic computing.</p>
-</blockquote>
-
-<p><a name="Xmos"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.xmos.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Xmos_logo.png" height="40" loading="lazy" alt="XMOS" /></a></div>
-
-<p><strong><a href="https://www.eetimes.com/xmos-adapts-xcore-into-aiot-crossover-processor/">XMOS adapts Xcore into AIoT ‘crossover processor’</a></strong></p>
-<blockquote>
-  <p>EE Times exclusive! The new chip targets AI-powered voice interfaces in IoT devices — “the most important AI workload at the endpoint.”</p>
-</blockquote>
-
-<p><a href="https://venturebeat.com/2020/02/12/xmos-unveils-xcore-ai-a-powerful-chip-designed-for-ai-processing-at-the-edge/">XMOS unveils Xcore.ai, a powerful chip designed for AI processing at the edge</a></p>
-<blockquote>
-  <p>The latest xcore.ai is a crossover chip designed to deliver high-performance AI, digital signal processing, control, and input/output in a single device with prices from $1.</p>
-</blockquote>
-
-<p><a name="DinoplusAI"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://dinoplus.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/DinoplusAI_logo.png" height="60" loading="lazy" alt="DinoplusAI" /></a></div>
-
-<blockquote>
-  <p>We design and produce AI processors and the software to run them in data centers. Our unique approach optimizes for inference with the focus on performance, power efficiency, and ease of use; and at the same time our approach enables cost-effective training. </p>
-</blockquote>
-
-<p><a name="Furiosa"></a></p>
+<a name="Furiosa"></a>
 
 <div align="center" class="vendor-logo"><a href="https://www.furiosa.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Furiosa_logo.png" height="60" loading="lazy" alt="FuriosaAI" /></a></div>
 
@@ -1027,139 +491,39 @@
 <blockquote>
   <p>Jan 2026 — FuriosaAI’s RNGD (Tensor Contraction Processor, TSMC 5nm, ~180W PCIe) entered volume shipment for LLM / agentic inference; NXT RNGD servers pack multiple cards into air-cooled, ~3kW-class systems.</p>
 </blockquote>
-
 <p><strong><a href="https://furiosa.ai/blog/furiosaai-partners-with-broadcom-to-build-next-generation-inference-platform-for-the-agentic-era">FuriosaAI partners with Broadcom on next-generation inference platform</a></strong></p>
 <blockquote>
   <p>May 2026 — Strategic collaboration to evolve TCP into a scale-up multi-die inference platform with Broadcom Ethernet/PCIe switching for hyperscale agentic workloads.</p>
 </blockquote>
 
-<p><a href="https://furiosa.ai/blog/furiosaai-equinixs-lisbon-data-center-press-release">Equinix Lisbon RNGD deployment</a> (Jul 2026)</p>
-
-<p><a name="Corerain"></a></p>
-
-<div align="center" class="vendor-logo"><a href="http://www.corerain.com/en"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Corerain_logo.png" height="60" loading="lazy" alt="Corerain" /></a></div>
-
-<blockquote>
-  <p>Corerain provides ultra-high performance AI acceleration chips and the world's first streaming engine-based AI development platform.</p>
-</blockquote>
-
-<p><a name="Perceive"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://perceive.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Perceive_logo.png" height="60" loading="lazy" alt="Perceive" /></a></div>
-
-<p><a href="https://venturebeat.com/2020/03/31/perceive-emerges-from-stealth-with-ergo-edge-ai-chip/">Perceive emerges from stealth with Ergo edge AI chip</a></p>
-<blockquote>
-  <p>On-device computing solutions startup Perceive emerged from stealth today with its first product: the Ergo edge processor for AI inference. CEO Steve Teig claims the chip, which is designed for consumer devices like security cameras, connected appliances, and mobile phones, delivers “breakthrough” accuracy and performance in its class.</p>
-</blockquote>
-
-<p><a name="SimpleMachines"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.simplemachines.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SimpleMachines_logo.png" height="60" loading="lazy" alt="SimpleMachines" /></a></div>
-
-<p><a href="https://www.design-reuse.com/news/49012/simplemachines-ai-chip-tsmc-16nm.html">SimpleMachines, Inc. Debuts First-of-its-Kind High Performance Chip</a></p>
-<blockquote>
-  <p>As traditional chip makers struggle to embrace the challenges presented by the rapidly evolving AI software landscape, a San Jose startup has announced it has working silicon and a whole new future-proof chip paradigm to address these issues.
-
-The SimpleMachines, Inc. (SMI) team – which includes leading research scientists and industry heavyweights formerly of Qualcomm, Intel and Sun Microsystems – has created a first-of-its-kind easily programmable, high-performance chip that will accelerate a wide variety of AI and machine-learning applications. </p>
-</blockquote>
-
-<p><a name="Neureality"></a></p>
-
-<div align="center" class="vendor-logo"><a href="https://www.neureality.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Neureality_logo.png" height="60" loading="lazy" alt="NeuReality" /></a></div>
-
-<p><strong><a href="https://techcrunch.com/2022/12/06/neureality-ai-accelerator-chips-startup-raises-35m/">NeuReality lands $35M to bring AI accelerator chips to market</a></strong></p>
-<blockquote>
-  <p>NeuReality, a startup developing AI inferencing accelerator chips, has raised $35 million in new venture capital.</p>
-</blockquote>
-
-<p><a href="https://www.electronicsmedia.info/2021/05/06/neureality-unveiled-nr1-p-a-novel-ai-centric-inference-platform/">NeuReality unveiled NR1-P, A novel AI-centric inference platform</a></p>
+<a name="Sagence"></a>
+<div align="center" class="vendor-logo"><a href="https://www.sagence.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AnalogInference_logo.png" height="60" loading="lazy" alt="Sagence AI" /></a></div>
+<div align="center" class="vendor-title"><h3>Sagence AI (ex–Analog Inference)</h3></div>
+<p><strong>Analog in-memory compute for datacenter inference (2024–2026)</strong></p>
 <blockquote>
-  <p>NeuReality has unveiled NR1-P, a novel AI-centric inference platform. NeuReality has already started demonstrating its AI-centric platform to customers and partners. NeuReality has redefined today’s outdated AI system architecture by developing an AI-centric inference platform based on a new type of System-on-Chip (SoC). </p>
+  <p>Rebranded from Analog Inference (Nov 2024). Continues analog compute-in-memory accelerators aimed at energy-efficient LLM / vision inference; active on the 2025–2026 AI infra circuit.</p>
 </blockquote>
 
-<p><a href="https://techcrunch.com/2021/02/10/neureality-raises-8m-for-its-novel-ai-inferencing-platform/">NeuReality raises $8M for its novel AI inferencing platform</a></p>
-<blockquote>
-  <p>NeuReality, an Israeli AI hardware startup that is working on a novel approach to improving AI inferencing platforms by doing away with the current CPU-centric model, is coming out of stealth today and announcing an $8 million seed round. </p>
-</blockquote>
- 
-<p><a name="AnalogInference"></a></p>
-<div align="center" class="vendor-logo"><a href="https://www.analog-inference.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AnalogInference_logo.png" height="60" loading="lazy" alt="Analog Inference" /></a></div>
+<p>
 
-<p><strong><a href="https://www.eenewsanalog.com/news/analog-inference-startup-raises-106-million">Analog inference startup raises $10.6 million</a></strong></p>
-<blockquote>
-  <p>The company is backed by Khosla Ventures and is developing its first generation of products for AI computing at the edge. The company raised $4.5 million shortly after its formation in March 2018, so the latest tranche brings the total raised to-date to $15.1 million</p>
-</blockquote>
+<p>
 
-<p><a name="Quadric"></a></p>
-<div align="center" class="vendor-logo"><a href="https://www.quadric.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Quatric_logo.png" height="60" loading="lazy" alt="Quadric" /></a></div>
+<p>
 
-<p><strong><a href="https://www.hpcwire.com/off-the-wire/quadric-announces-unified-silicon-and-software-platform-optimized-for-on-device-ai/">Quadric Announces Unified Silicon and Software Platform Optimized for On-Device AI</a></strong></p>
-<blockquote>
-  <p>BURLINGAME, Calif., June 22, 2021 — Quadric (quadric.io), an innovator in high-performance edge processing, has introduced a unified silicon and software platform that unlocks the power of on-device AI. </p>
-</blockquote> 
-
-<p><a name="EdgeQ"></a></p>
-<div align="center" class="vendor-logo"><a href="https://edgeq.io/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/EdgeQ_logo.png" height="60" loading="lazy" alt="EdgeQ" /></a></div>
-
-<p><strong><a href="https://techcrunch.com/2021/01/26/edgeq-reveals-more-details-behind-its-next-gen-5g-ai-chip/">EdgeQ reveals more details behind its next-gen 5G/AI chip</a></strong></p>
-<blockquote>
-  <p>5G is the current revolution in wireless technology, and every chip company old and new is trying to burrow their way into this ultra-competitive — but extremely lucrative — market. One of the most interesting new players in the space is EdgeQ, a startup with a strong technical pedigree via Qualcomm that we covered last year after it raised a nearly $40 million Series A.</p>
-</blockquote> 
- 
-<p><a name="Innatera"></a></p>
-<div align="center" class="vendor-logo"><a href="http://www.innatera.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Innatera_logo.png" height="60" loading="lazy" alt="Innatera" /></a></div>
-
-<p><strong><a href="https://www.eetimes.com/innatera-unveils-neuromorphic-ai-chip-to-accelerate-spiking-networks/">Innatera Unveils Neuromorphic AI Chip to Accelerate Spiking Networks</a></strong></p>
-<blockquote>
-  <p>Innatera, the Dutch startup making neuromorphic AI accelerators for spiking neural networks, has produced its first chips, gauged their performance, and revealed details of their architecture.</p>
-</blockquote> 
- 
-<p><a name="Ceremorphic"></a></p>
-<div align="center" class="vendor-logo"><a href="https://ceremorphic.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Ceremorphic_logo.png" height="60" loading="lazy" alt="Ceremorphic" /></a></div>
-
-<p><strong><a href="https://www.eetimes.com/redpine-founder-launches-ai-processor-startup/">Redpine Founder Launches AI Processor Startup</a></strong></p>
-<blockquote>
-  <p>Ceremorphic, an AI chip startup emerging from stealth mode this week, is readying a heterogeneous AI processor aimed at model training in data centers, automotive, high-performance computing, robotics and other emerging applications.</p>
-</blockquote> 
- 
-<p><a name="Aspinity"></a></p>
-<div align="center" class="vendor-logo"><a href="https://www.aspinity.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Aspinity_logo.png" height="60" loading="lazy" alt="Aspinity" /></a></div>
-
-<p><strong><a href="https://embeddedcomputing.com/technology/analog-and-power/analog-semicundoctors-sensors/aspinity-analog-ml-chip-allows-battery-powered-always-on">Aspinity Analog ML Chip Allows Battery-Powered “Always On”</a></strong></p>
-<blockquote>
-  <p>Machine learning (ML) is all about massive amounts of processing, DSP, etc., right? Maybe not, according to the team at Aspinity. The company continues to push ahead on the analog front. The latest member of the company’s analogML family, the AML100, operates completely in the analog domain. As a result, it can reduce always-on system power by 95% (for the record, we had to walk through this a couple of times before I believed them).</p>
-</blockquote> 
- 
-<p><a name="Teramem"></a></p>
-<div align="center" class="vendor-logo"><a href="https://www.tetramem.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Teramem_logo.png" height="60" loading="lazy" alt="TetraMem" /></a></div>
-
-<p><strong><a href="https://www.tetramem.com/posts/TetraMem-Technology-Debut-at-Linley">TetraMem enjoyed an exciting public debut of our analog in-memory compute technology at the Linley Spring 2022 Processor Conference.</a></strong></p>
-<blockquote>
-  <p></p>
-</blockquote> 
- 
-<p><a name="d-matrix"></a></p>
+<a name="d-matrix"></a>
 <div align="center" class="vendor-logo"><a href="https://www.d-matrix.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/d-matrix_logo.png" height="60" loading="lazy" alt="d-Matrix" /></a></div>
-
-<p><strong><a href="https://www.reuters.com/technology/ai-chip-startup-d-matrix-raises-110-mln-with-backing-microsoft-2023-09-06/">Exclusive: AI chip startup d-Matrix raises $110 million with backing from Microsoft</a></strong></p>
-<blockquote>
-  <p>Sept 6 (Reuters) - Silicon Valley-based artificial intelligence chip startup d-Matrix has raised $110 million from investors that include Microsoft Corp (MSFT.O) at a time when many chip companies are struggling to raise cash.</p>
-</blockquote> 
-
-<p><a href="https://www.forbes.com/sites/karlfreund/2022/06/21/d-matrix-ai-chip-promises-efficient-transformer-processing/">D-Matrix AI chip promises efficient transformer processing</a></p>
+<p><strong><a href="https://www.d-matrix.ai/">d-Matrix Corsair — digital in-memory compute for inference</a></strong></p>
 <blockquote>
-  <p>The startup combines digital in-memory compute and chiplet implementations for data-center-grade inference.</p>
-</blockquote> 
- 
+  <p>2024–2026 — Corsair accelerators target low-latency / high-efficiency datacenter LLM inference with digital compute-in-memory; expanding software and cloud partner footprint.</p>
+</blockquote>
 
-<p><a name="Etched"></a></p>
+<a name="Etched"></a>
 <div align="center"><h3><a href="https://www.etched.com/">Etched</a></h3></div>
 
 <p><strong><a href="https://techcrunch.com/2026/06/30/nvidia-competitor-etched-hits-5b-valuation-1b-in-sales-for-ai-chip/">Nvidia competitor Etched hits $5B valuation, $1B in sales for AI chip</a></strong></p>
 <blockquote>
   <p>Jun 2026 — Etched reports TSMC-manufactured transformer-specialized ASICs powering “frontier inference clusters,” with ~$800M raised to date (including a $500M round at a $5B valuation) and ~$1B in booked system orders.</p>
 </blockquote>
-
 <p><a href="https://techcrunch.com/2024/06/25/etched-is-building-an-ai-chip-that-only-runs-transformer-models/">Etched is building an AI chip that only runs one type of model</a></p>
 <blockquote>
   <p>2024 Series A coverage of Sohu: an ASIC purpose-built for Transformer inference (rather than general-purpose GPU programmability), aiming for higher tokens/sec and better energy efficiency on LLM serving.</p>
@@ -1167,9 +531,10 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 <div align="center" class="section-title"><h2><a name="China_AI_Chips"></a>V. China AI Accelerator Vendors</h2></div>
 <hr class="section-rule" />
+
 <p>Domestic training/inference accelerators have become central under export-control and procurement policies. Coverage below highlights widely reported product lines; treat performance claims as vendor-/press-reported unless independently benchmarked.</p>
 
-<p><a name="HuaweiAscend"></a></p>
+<a name="HuaweiAscend"></a>
 <div align="center" class="vendor-logo"><a href="https://www.huawei.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HUAWEI_logo.png" height="40" loading="lazy" alt="HUAWEI logo" /></a>
 &nbsp;&nbsp;
 <a href="https://www.hisilicon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HiSilicon_logo.png" height="40" loading="lazy" alt="HiSilicon logo" /></a></div>
@@ -1180,9 +545,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>2025 — Huawei scaled Ascend 910B/910C deployments and launched Atlas 900 A3 SuperPoD (up to 384× Ascend 910C), also referred to in cloud form as CloudMatrix384-class supernodes, for large-model training and serving. Ascend 950-class follow-ons are on Huawei’s roadmap.</p>
 </blockquote>
 
-<p><a href="https://arxiv.org/abs/2506.12708">Serving Large Language Models on Huawei CloudMatrix384</a> (technical paper)</p>
-
-<p><a name="Cambricon"></a></p>
+<a name="Cambricon"></a>
 <div align="center" class="vendor-logo"><a href="https://www.cambricon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cambricon_logo.png" height="50" loading="lazy" alt="Cambricon" /></a></div>
 <div align="center"><h3>Cambricon (寒武纪)</h3></div>
 
@@ -1191,12 +554,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Reports describe aggressive 2026 volume plans for Siyuan / MLU-class accelerators (including Siyuan 590 and next-gen 690 messaging), serving Chinese internet/cloud customers as a leading pure-play domestic AI-chip vendor.</p>
 </blockquote>
 
-<p><a href="https://awesomeagents.ai/hardware/cambricon-mlu590/">Cambricon MLU590 overview</a></p>
-<blockquote>
-  <p>MLU500-series cloud accelerator commonly cited for China data-center training/inference deployments with MLU-Link multi-card interconnect and Cambricon’s NeuWare / BANG software stack.</p>
-</blockquote>
-
-<p><a name="Biren"></a></p>
+<a name="Biren"></a>
 <div align="center"><h3><a href="https://www.birentech.com/">Biren Technology (壁仞)</a></h3></div>
 
 <p><strong><a href="https://www.reuters.com/world/asia-pacific/china-ai-chipmaker-biren-surges-82-hong-kong-debut-kicking-off-2026-listings-2026-01-02/">China AI chipmaker Biren soars in Hong Kong debut</a></strong></p>
@@ -1204,7 +562,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Jan 2026 — Shanghai Biren Technology listed in Hong Kong (6082.HK), raising ~HK$5.58B. Best known for BR100-class GPGPU / AI accelerators positioned as high-performance domestic alternatives.</p>
 </blockquote>
 
-<p><a name="MooreThreads"></a></p>
+<a name="MooreThreads"></a>
 <div align="center"><h3><a href="https://www.mthreads.com/">Moore Threads (摩尔线程)</a></h3></div>
 
 <p><strong><a href="https://www.cnbc.com/2025/12/05/china-nvidia-moore-threads-trading-debut-1-billion-listing-ipo-shanghai-gpu-enflame-biren.html">Moore Threads surges over 400% on Shanghai trading debut</a></strong></p>
@@ -1212,25 +570,24 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Dec 2025 — Full-function GPU vendor (MTT S4000 / S5000 class AI cards, MUSA software stack) completed a ~$1.1B STAR Market IPO amid China’s domestic GPU push.</p>
 </blockquote>
 
-<p><a name="Enflame"></a></p>
-<div align="center" class="vendor-logo"><a href="https://github.com/EnflameTechnology"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Enflame_logo.png" height="50" loading="lazy" alt="Enflame" /></a></div>
-<div align="center"><h3>Enflame (燧原)</h3></div>
+<p>
 
-<p><strong><a href="https://www.hpcwire.com/off-the-wire/enflame-technology-announces-cloudblazer-with-dtu-chip-on-globalfoundries-12lp-finfet-platform-for-data-center-training/">Enflame CloudBlazer / DTU AI accelerators</a></strong></p>
+<a name="Enflame"></a>
+<div align="center" class="vendor-title"><h3><a href="https://www.enflame-tech.com/">Enflame (燧原)</a></h3></div>
+<p><strong>Enflame CloudBlazer / iGCU accelerators</strong></p>
 <blockquote>
-  <p>Shanghai-based accelerator vendor offering CloudBlazer training/inference products (DTU architecture) for domestic intelligent-computing centers; frequently grouped with China’s “GPU dragons” alongside Biren, Moore Threads, and MetaX. Open-source ecosystem links: <a href="https://github.com/EnflameTechnology">EnflameTechnology on GitHub</a>.</p>
+  <p>2024–2026 — Domestic training/inference GPUs (CloudBlazer series) deployed in China intelligent-computing centers with TopsRider software stack updates for LLM workloads.</p>
 </blockquote>
 
-<p><a name="Horizon"></a></p>
+<a name="Horizon"></a>
 <div align="center" class="vendor-logo"><a href="https://www.horizon.auto/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Horizon_Robotics_logo.png" height="50" loading="lazy" alt="Horizon Robotics" /></a></div>
-<div align="center"><h3>Horizon Robotics (地平线)</h3></div>
-
-<p><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Horizon Journey 6 / BPU Nash automotive AI processors</a></strong></p>
+<div align="center" class="vendor-title"><h3>Horizon Robotics (地平线)</h3></div>
+<p><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Horizon Journey 6 / BPU Nash</a></strong></p>
 <blockquote>
-  <p>Leading China ADAS / autonomous-driving compute vendor with Journey-series BPU SoCs (Journey 6 adds BPU Nash with stronger Transformer support) widely designed into passenger vehicles; focuses on efficient vision/transformer perception at the edge rather than datacenter LLM training.</p>
+  <p>2024–2026 — Journey-series automotive AI SoCs (Journey 6 with BPU Nash / stronger Transformer support) widely designed into China passenger vehicles for L2+ assisted driving.</p>
 </blockquote>
 
-<p><a name="BlackSesame"></a></p>
+<a name="BlackSesame"></a>
 <div align="center" class="vendor-logo"><a href="https://www.blacksesame.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blacksesame_logo.png" height="50" loading="lazy" alt="Black Sesame" /></a></div>
 <div align="center"><h3>Black Sesame Technologies (黑芝麻智能)</h3></div>
 
@@ -1239,9 +596,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Hong Kong-listed (02533.HK, Aug 2024) automotive AI SoC vendor. Huashan A1000-family chips target L2+/L3 assisted driving with automotive-grade safety certifications and are reported in mass-production vehicle models; Huashan A2000 messaging extends toward higher-performance / cross-domain AI inference.</p>
 </blockquote>
 
-<p><a href="https://www.caixinglobal.com/2024-08-01/chinese-auto-chip-maker-black-sesame-to-raise-143-million-in-hong-kong-ipo-102222110.html">Caixin: Black Sesame Hong Kong IPO</a></p>
-
-<p><a name="MetaX"></a></p>
+<a name="MetaX"></a>
 <div align="center" class="vendor-title"><h3><a href="https://www.metax-tech.com/en/about/about.html">MetaX (沐曦)</a></h3></div>
 
 <p><strong><a href="https://www.caixinglobal.com/2026-03-26/chinese-gpu-maker-metax-doubles-revenue-amid-push-for-domestic-chips-102427800.html">Chinese GPU Maker MetaX Doubles Revenue Amid Push for Domestic Chips</a></strong></p>
@@ -1249,9 +604,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Shanghai MetaX (STAR: 688802.SH, listed Dec 2025) ships C-series training/inference GPUs (C500 mass production; C600 domestic-process follow-on) with the MXMACA software stack for AI clusters and intelligent-computing centers.</p>
 </blockquote>
 
-<p><a href="https://www.chinadaily.com.cn/a/202607/09/WS6a4f3978a310986e2b4645f5.html">MetaX scales production to meet rising AI chip demand</a></p>
-
-<p><a name="Hygon"></a></p>
+<a name="Hygon"></a>
 <div align="center" class="vendor-title"><h3><a href="https://www.hygon.cn/">Hygon (海光) DCU</a></h3></div>
 
 <p><strong><a href="https://www.trendforce.com/news/2026/04/29/news-huawei-ascend-cambricon-and-hygon-completed-day-0-adaptation-to-deepseek-v4/">Hygon DCU completes Day-0 adaptation for DeepSeek-V4</a></strong></p>
@@ -1259,9 +612,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Hygon’s Deep Computing Unit (DCU) GPGPU-class accelerators and DTK software stack are widely used in China for large-model training/inference; 2025–2026 coverage highlights same-day DeepSeek model adaptations alongside Ascend and Cambricon.</p>
 </blockquote>
 
-<p><a href="https://github.com/HYGON-AI/dcu-inference-cookbook">HYGON-AI DCU inference cookbook</a> (open deployment guides)</p>
-
-<p><a name="Iluvatar"></a></p>
+<a name="Iluvatar"></a>
 <div align="center" class="vendor-title"><h3><a href="https://www.iluvatar.com/">Iluvatar CoreX (天数智芯)</a></h3></div>
 
 <p><strong><a href="https://www.scmp.com/tech/big-tech/article/3341368/iluvatar-corex-targets-nvidias-rubin-gpu-road-map-amid-china-chip-push">Iluvatar CoreX targets Nvidia’s Rubin with GPU road map</a></strong></p>
@@ -1269,7 +620,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
   <p>Jan 2026 — Hong Kong-listed Iluvatar CoreX outlined a multi-generation GPU architecture roadmap (Tianshu / Tianxuan / Tianji / Tianquan messaging) aiming to close the gap with Nvidia Hopper→Blackwell→Rubin class platforms, with BI-V150-class products already in AI training/inference deployments.</p>
 </blockquote>
 
-<p><a name="AIChipCompilers"></a></p>
+<a name="AIChipCompilers"></a>
 
 <div align="center" class="section-title"><h2 id="compilers-heading">AI Chip Compilers</h2></div>
 <div class="resource-list">
@@ -1296,7 +647,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 </div>
 
-<p><a name="AIChipBenchmarks"></a></p>
+<a name="AIChipBenchmarks"></a>
 
 <div align="center" class="section-title"><h2 id="benchmarks-heading">AI Chip Benchmarks</h2></div>
 <div class="resource-list">
@@ -1316,7 +667,7 @@ The SimpleMachines, Inc. (SMI) team – which includes leading research scientis
 
 </div>
 
-<p><a name="Reference"></a></p>
+<a name="Reference"></a>
 
 <div align="center" class="section-title"><h2>Reference</h2></div>
 
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From 763f1c03599a52d7ed472cf16374dc9600963b9f Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 03:00:23 +0000
Subject: [PATCH 07/13] Tidy Reference archive notes and leftover empty
 paragraphs

Clarify the Reference section as background reading and remove stale
list items / empty paragraph tags left from the content prune.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 62f5e35..20a21ba 100644
--- a/README.md
+++ b/README.md
@@ -674,16 +674,19 @@
 <hr class="section-rule" />
 
 <div class="resource-list">
-      
-1. <a href="https://meanderful.blogspot.jp/2017/06/fpgas-and-ai-processors-dnn-and-cnn-for.html">FPGAs and AI processors: DNN and CNN for all</a><br>
-2. <a href="http://www.nanalyze.com/2017/05/12-ai-hardware-startups-new-ai-chips/">12 AI Hardware Startups Building New AI Chips</a><br>
-3. <a href="http://eyeriss.mit.edu/tutorial.html">Tutorial on Hardware Architectures for Deep Neural Networks</a><br>
-4. <strong><a href="https://nicsefc.ee.tsinghua.edu.cn/projects/neural-network-accelerator/">Neural Network Accelerator Comparison</a></strong><br>
-5. "White Paper on AI Chip Technologies 2018". You can download it from <a href="https://cloud.tsinghua.edu.cn/f/9aa0a4f0a5684cc48495/?dl=1">here</a>, or <a href="https://drive.google.com/open?id=1ieDm0bpjVWl5MnSESRs92EcmoSzG5vcm">Google drive.</a><br>
-6. <strong>"What We Talk About When We Talk About AI Chip". <a href="https://mp.weixin.qq.com/s/SbX5yz5d3GXaLcl15DO6OQ">#1</a>,  <a href="https://mp.weixin.qq.com/s/zvgDgKpIMIRLFUEW0fFOeg">#2</a>,  <a href="https://mp.weixin.qq.com/s/CKHs5yblcMur4h2BwUBICw">#3</a>,  <a href="https://mp.weixin.qq.com/s/hFnHhaWWYTFRUsD3HlMbLw">#4</a></strong><br>
-7. <strong><a href="https://birenresearch.github.io/AIChip_Paper_List/">AI Chip Paper List</a></strong><br>
-8. <strong><a href="https://khairy2011.medium.com/tpu-vs-gpu-vs-cerebras-vs-graphcore-a-fair-comparison-between-ml-hardware-3f5a19d89e38">TPU vs GPU vs Cerebras vs Graphcore: A Fair Comparison between ML Hardware</a></strong><br>
-
+<p><em>Archive / background reading (not a current vendor list).</em></p>
+1. <strong><a href="https://birenresearch.github.io/AIChip_Paper_List/">AI Chip Paper List</a></strong><br>
+2. <strong><a href="https://nicsefc.ee.tsinghua.edu.cn/projects/neural-network-accelerator/">Neural Network Accelerator Comparison</a></strong> (Tsinghua)<br>
+3. <a href="http://eyeriss.mit.edu/tutorial.html">Tutorial on Hardware Architectures for Deep Neural Networks</a> (MIT Eyeriss)<br>
+4. <strong>"What We Talk About When We Talk About AI Chip"</strong> —
+   <a href="https://mp.weixin.qq.com/s/SbX5yz5d3GXaLcl15DO6OQ">#1</a> ·
+   <a href="https://mp.weixin.qq.com/s/zvgDgKpIMIRLFUEW0fFOeg">#2</a> ·
+   <a href="https://mp.weixin.qq.com/s/CKHs5yblcMur4h2BwUBICw">#3</a> ·
+   <a href="https://mp.weixin.qq.com/s/hFnHhaWWYTFRUsD3HlMbLw">#4</a><br>
+5. <a href="https://khairy2011.medium.com/tpu-vs-gpu-vs-cerebras-vs-graphcore-a-fair-comparison-between-ml-hardware-3f5a19d89e38">TPU vs GPU vs Cerebras vs Graphcore</a> (comparison essay)<br>
+6. "White Paper on AI Chip Technologies 2018" —
+   <a href="https://cloud.tsinghua.edu.cn/f/9aa0a4f0a5684cc48495/?dl=1">download</a> ·
+   <a href="https://drive.google.com/open?id=1ieDm0bpjVWl5MnSESRs92EcmoSzG5vcm">Google Drive</a><br>
 </div>
 
 <div align="center">

From df4688edcbe18278057637b0610d5e98e50d29b0 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 03:07:12 +0000
Subject: [PATCH 08/13] Regenerate landscape map with company logos

Replace the text-only v0.8 chart with a logo-tile layout closer to
v0.7, including white-logo inversion so light marks stay visible.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 resource/AI_Chip_Landscape_v0p8.png        | Bin 104563 -> 334329 bytes
 resource/AI_Chip_Landscape_v0p8_MidRes.png | Bin 0 -> 318997 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 resource/AI_Chip_Landscape_v0p8_MidRes.png

diff --git a/resource/AI_Chip_Landscape_v0p8.png b/resource/AI_Chip_Landscape_v0p8.png
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From d5279ec51f142ff6e4687fce5773ac53fc881cf7 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 03:15:23 +0000
Subject: [PATCH 09/13] Add missing vendor logos and regenerate landscape map

Supplement OpenAI, Anthropic, Groq, Broadcom, Etched, Biren, MetaX,
Moore Threads, Hygon, and Iluvatar logo assets; regenerate the v0.8
logo-tile landscape so previously text-only cells show marks.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 resource/AI_Chip_Landscape_v0p8.png        | Bin 334329 -> 332993 bytes
 resource/AI_Chip_Landscape_v0p8_MidRes.png | Bin 318997 -> 316558 bytes
 resource/Anthropic_logo.png                | Bin 0 -> 1096 bytes
 resource/Biren_logo.png                    | Bin 0 -> 3035 bytes
 resource/Broadcom_logo.png                 | Bin 0 -> 6685 bytes
 resource/Etched_logo.png                   | Bin 0 -> 2587 bytes
 resource/Graphcore_mark.png                | Bin 0 -> 30209 bytes
 resource/Groq_logo.png                     | Bin 0 -> 14727 bytes
 resource/Hygon_logo.png                    | Bin 0 -> 10406 bytes
 resource/Iluvatar_logo.png                 | Bin 0 -> 83463 bytes
 resource/MetaX_logo.png                    | Bin 0 -> 8137 bytes
 resource/MooreThreads_logo.png             | Bin 0 -> 22101 bytes
 resource/OpenAI_logo.png                   | Bin 0 -> 1096 bytes
 13 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 resource/Anthropic_logo.png
 create mode 100644 resource/Biren_logo.png
 create mode 100644 resource/Broadcom_logo.png
 create mode 100644 resource/Etched_logo.png
 create mode 100644 resource/Graphcore_mark.png
 create mode 100644 resource/Groq_logo.png
 create mode 100644 resource/Hygon_logo.png
 create mode 100644 resource/Iluvatar_logo.png
 create mode 100644 resource/MetaX_logo.png
 create mode 100644 resource/MooreThreads_logo.png
 create mode 100644 resource/OpenAI_logo.png

diff --git a/resource/AI_Chip_Landscape_v0p8.png b/resource/AI_Chip_Landscape_v0p8.png
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diff --git a/resource/OpenAI_logo.png b/resource/OpenAI_logo.png
new file mode 100644
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From bb52c21191c08942d319ffd709a7c704f0742956 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 03:17:42 +0000
Subject: [PATCH 10/13] Fix newly added logos so they render on the landscape
 map

Re-export OpenAI/Anthropic/Groq/Broadcom/Biren/MetaX marks with
correct transparency, and regenerate the v0.8 logo-tile landscape.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 resource/AI_Chip_Landscape_v0p8.png        | Bin 332993 -> 354795 bytes
 resource/AI_Chip_Landscape_v0p8_MidRes.png | Bin 316558 -> 327933 bytes
 resource/Anthropic_logo.png                | Bin 1096 -> 14002 bytes
 resource/Biren_logo.png                    | Bin 3035 -> 91031 bytes
 resource/Broadcom_logo.png                 | Bin 6685 -> 15379 bytes
 resource/Etched_logo.png                   | Bin 2587 -> 5689 bytes
 resource/Graphcore_mark.png                | Bin 30209 -> 32728 bytes
 resource/Groq_logo.png                     | Bin 14727 -> 13749 bytes
 resource/Hygon_logo.png                    | Bin 10406 -> 7807 bytes
 resource/Iluvatar_logo.png                 | Bin 83463 -> 94354 bytes
 resource/MetaX_logo.png                    | Bin 8137 -> 15564 bytes
 resource/MooreThreads_logo.png             | Bin 22101 -> 118552 bytes
 resource/OpenAI_logo.png                   | Bin 1096 -> 27365 bytes
 13 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/resource/AI_Chip_Landscape_v0p8.png b/resource/AI_Chip_Landscape_v0p8.png
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diff --git a/resource/Graphcore_mark.png b/resource/Graphcore_mark.png
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From 08dfce441f1f36a3504edbe8f389235f3896c0c1 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 03:32:41 +0000
Subject: [PATCH 11/13] =?UTF-8?q?Restructure=20vendor=20pages:=20intro/his?=
 =?UTF-8?q?tory=20+=201=E2=80=932=20latest=20+=20header=20glance?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Replace the news-list model with a consistent template: a short AI-chip
intro (with lineage for NVIDIA/Google/AMD and similar), a Latest section
limited to one or two updates, and an At a glance one-liner per company
near the top of the page for periodic refresh.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md             | 816 +++++++++++++++++++++---------------------
 assets/css/style.scss |  50 ++-
 2 files changed, 454 insertions(+), 412 deletions(-)

diff --git a/README.md b/README.md
index 20a21ba..99859f0 100644
--- a/README.md
+++ b/README.md
@@ -5,24 +5,63 @@
 <p class="wechat-line"><strong>WeChat / 微信公众号</strong> · <a href="https://mp.weixin.qq.com/mp/appmsgalbum?action=getalbum&amp;__biz=MzI3MDQ2MjA3OA==&amp;scene=1&amp;album_id=1374108991751782402&amp;count=3#wechat_redirect">StarryHeavensAbove</a> — AI chip articles</p>
 <div class="wechat-qr"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/qrcode_for_weichat_258.jpg" height="100" loading="lazy" alt="WeChat QR code" /></div>
 <div class="landscape-map"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AI_Chip_Landscape_v0p8.png" loading="lazy" alt="AI Chip Landscape v0.8 (2026)" /></div>
-<p class="page-intro">A living directory of AI accelerators (ICs &amp; IPs) — hyperscaler custom silicon, GPU/NPU vendors, IP houses, startups, and China domestic accelerators. Prefer official / primary sources; performance claims are often vendor-reported.</p>
+<p class="page-intro">A living directory of AI accelerators (ICs &amp; IPs) — each entry has a short field intro (plus lineage where relevant) and 1–2 latest updates. Prefer official / primary sources; performance claims are often vendor-reported.</p>
 </header>
 
- 
-<div align="center" class="section-title"><h2>Latest updates</h2></div>
-<p class="refresh-note" align="center"><em>Last refreshed: July 2026 — defunct vendors removed; pre-2024 entries cleared (except NVIDIA/Google lineage); landscape map v0.8.</em></p>
+<div align="center" class="section-title"><h2>At a glance</h2></div>
+<p class="refresh-note" align="center"><em>One-line latest status per company — updated periodically. Last refreshed: July 2026.</em></p>
 <hr class="section-rule" />
-
-<ul class="updates-list">
-<li><strong>Hygiene</strong>: removed shut-down / pivoted AI-chip vendors; cleared pre-2024 news for other orgs.</li>
-<li><strong>NVIDIA</strong>: full GPU AI lineage CUDA/Pascal → Volta → Hopper → Blackwell → <a href="#Nvidia">Rubin</a>.</li>
-<li><strong>Google</strong>: full <a href="#Google">TPU</a> lineage v1 → Trillium (v6e) → Ironwood (v7).</li>
-<li><strong>AMD / Intel</strong>: <a href="#AMD">MI350</a>; <a href="#Habana">Gaudi 3</a>.</li>
-<li><strong>Custom silicon</strong>: AWS <a href="#Amazon_AWS">Trainium2</a>, Azure <a href="#Microsoft">Maia</a>, Meta <a href="#Facebook">MTIA</a>, <a href="#OpenAI">Jalapeño</a>, <a href="#Anthropic">Anthropic</a> deals.</li>
-<li><strong>Edge</strong>: <a href="#SiMa">SiMa</a>, <a href="#Hailo">Hailo-10H</a>, <a href="#Tesla">Tesla AI5</a>, <a href="#BlackSesame">Black Sesame</a>.</li>
-<li><strong>Active startups</strong>: <a href="#Cerebras">Cerebras</a>, <a href="#Furiosa">Furiosa</a>, <a href="#Etched">Etched</a>, <a href="#SambaNova">SambaNova</a>, <a href="#Mythic">Mythic</a>, <a href="#Kinara">Kinara</a>, …</li>
-<li><strong>China</strong>: <a href="#China_AI_Chips">Ascend / Cambricon / Biren / MetaX / Hygon / Iluvatar</a> …</li>
-<li><strong>Map</strong>: regenerated AI Chip Landscape <strong>v0.8</strong> (2026).</li>
+<ul class="glance-list">
+<li><a href="#Nvidia"><strong>NVIDIA</strong></a> — Vera Rubin platform announced for H2 2026; Blackwell NVL72 in volume AI-factory deployments.</li>
+<li><a href="#Intel"><strong>Intel / Habana</strong></a> — Gaudi 3 shipping for enterprise GenAI train/infer with Ethernet scale-out OEM/cloud partners.</li>
+<li><a href="#AMD"><strong>AMD</strong></a> — Instinct MI350 shipping; Meta partnership expands multi-GW MI450 / Helios rack deployments.</li>
+<li><a href="#Qualcomm"><strong>Qualcomm</strong></a> — Snapdragon 8 Elite Hexagon NPU leads on-device GenAI; Cloud AI 100 remains the inference SKU.</li>
+<li><a href="#Samsung"><strong>Samsung</strong></a> — Exynos 2500 (3nm GAA) ships with a substantially upgraded on-device NPU.</li>
+<li><a href="#MediaTek"><strong>MediaTek</strong></a> — Dimensity 9400 NPU 890 targets agentic on-device AI (LoRA, video, MoE LLMs).</li>
+<li><a href="#Marvell"><strong>Marvell</strong></a> — Major custom AI ASIC + DPU partner for hyperscaler accelerators (Trainium/Maia-class programs).</li>
+<li><a href="#Broadcom"><strong>Broadcom</strong></a> — OpenAI Jalapeño / multi-GW XPU deal; extended Meta MTIA partnership; long-time Google TPU partner.</li>
+<li><a href="#Google"><strong>Google</strong></a> — Ironwood (TPU v7) and Trillium (v6e) power Cloud TPU; multi-GW Anthropic capacity deals.</li>
+<li><a href="#Amazon_AWS"><strong>AWS</strong></a> — Trainium2 Trn2 UltraServers generally available; Inferentia2 continues cost-efficient LLM serving.</li>
+<li><a href="#Microsoft"><strong>Microsoft</strong></a> — Azure Maia 100 deployed for Azure AI / Copilot-class workloads; next Maia gens in design.</li>
+<li><a href="#OpenAI"><strong>OpenAI</strong></a> — First custom inference ASIC “Jalapeño” with Broadcom; initial deployments targeted end of 2026.</li>
+<li><a href="#Anthropic"><strong>Anthropic</strong></a> — Expanded Google TPU + Broadcom capacity (~3.5 GW from 2027) atop AWS Trainium and NVIDIA GPUs.</li>
+<li><a href="#Apple"><strong>Apple</strong></a> — M5 / M5 Pro / M5 Max add GPU Neural Accelerators and a faster Neural Engine for Apple Intelligence.</li>
+<li><a href="#Alibaba"><strong>Alibaba</strong></a> — T-Head PPU / Zhenwu family deployed in Alibaba Cloud and China Unicom-scale clusters.</li>
+<li><a href="#Baidu"><strong>Baidu</strong></a> — Kunlunxin P800-class GPUs remain core to Baidu Cloud / ERNIE train and serve.</li>
+<li><a href="#Tencent_Cloud"><strong>Tencent</strong></a> — Zixiao and partner accelerators continue scaling LLM/recommendation workloads on Tencent Cloud.</li>
+<li><a href="#Facebook"><strong>Meta</strong></a> — Multi-generation MTIA (300–500 class) with Broadcom; modular chiplets through 2026–2027.</li>
+<li><a href="#Tesla"><strong>Tesla</strong></a> — AI5 taped out (Apr 2026); AI6 / Dojo3 in progress; vehicles near-term stay on AI4.</li>
+<li><a href="#ARM"><strong>Arm</strong></a> — Ethos-U85 micro-NPU adds native transformer ops for edge/IoT GenAI with Corstone-320.</li>
+<li><a href="#Synopsys"><strong>Synopsys</strong></a> — ARC NPX NPU IP scales multi-TOPS configurations for auto/consumer/datacenter SoCs.</li>
+<li><a href="#Imagination"><strong>Imagination</strong></a> — PowerVR NNA + Continuum software for edge/automotive transformer-class networks.</li>
+<li><a href="#CEVA"><strong>CEVA</strong></a> — NeuPro-class NPU IP and DNN compiler for embedded, automotive, and IoT inference.</li>
+<li><a href="#Cadence"><strong>Cadence</strong></a> — Tensilica AI Platform / NeuroWeave updates for on-device and automotive transformers.</li>
+<li><a href="#VeriSilicon"><strong>VeriSilicon</strong></a> — VIP/Acuity NPU IP and turnkey design services widely used in China and global edge SoCs.</li>
+<li><a href="#Cerebras"><strong>Cerebras</strong></a> — WSE-3 / CS-3 wafer-scale systems shipping for large-model train and high-throughput inference.</li>
+<li><a href="#Graphcore"><strong>Graphcore</strong></a> — Now a SoftBank subsidiary; continuing next-gen IPU / AI compute under SoftBank backing.</li>
+<li><a href="#Tenstorrent"><strong>Tenstorrent</strong></a> — Wormhole shipping; Blackhole next — Tensix chiplet meshes with open TT-Metalium software.</li>
+<li><a href="#Etched"><strong>Etched</strong></a> — ~$5B valuation / ~$1B booked orders for transformer-specialized Sohu inference ASICs.</li>
+<li><a href="#Groq"><strong>Groq</strong></a> — LPU technology licensed into NVIDIA’s Vera Rubin AI-factory stack (GTC 2026).</li>
+<li><a href="#SambaNova"><strong>SambaNova</strong></a> — SN50 RDU for agentic AI; $350M+ Series E; SoftBank early deploy; Intel collab; H2 2026 ship.</li>
+<li><a href="#Mythic"><strong>Mythic</strong></a> — $125M Series D + Videantis acquisition for hybrid analog/digital energy-efficient inference.</li>
+<li><a href="#Hailo"><strong>Hailo</strong></a> — Hailo-10H generally available for edge LLMs/VLMs (~40 TOPS INT4 class, ~2.5W).</li>
+<li><a href="#Furiosa"><strong>FuriosaAI</strong></a> — RNGD in mass production; Broadcom partnership for next-gen multi-die inference platform.</li>
+<li><a href="#Kinara"><strong>Kinara</strong></a> — NXP acquiring Kinara (ex–Deep Vision) for industrial/automotive edge NPU portfolio.</li>
+<li><a href="#SiMa"><strong>SiMa.ai</strong></a> — Modalix MLSoC in production for Physical AI (robots/industrial) under ~10W with LLiMa LLMs.</li>
+<li><a href="#d-matrix"><strong>d-Matrix</strong></a> — Corsair digital in-memory compute accelerators targeting efficient datacenter LLM inference.</li>
+<li><a href="#Blaize"><strong>Blaize</strong></a> — Nasdaq-listed (BZAI); GSP architecture for industrial/smart-city edge vision and GenAI.</li>
+<li><a href="#SynSense"><strong>SynSense</strong></a> — Speck/Xylo neuromorphic chips; iniVation collab extends event-based vision + edge AI.</li>
+<li><a href="#Sagence"><strong>Sagence AI</strong></a> — Rebranded from Analog Inference; analog CIM accelerators for efficient LLM/vision inference.</li>
+<li><a href="#HuaweiAscend"><strong>Huawei Ascend</strong></a> — Ascend 910B/910C + Atlas 900 A3 SuperPoD (CloudMatrix384-class); 950-class on roadmap.</li>
+<li><a href="#Cambricon"><strong>Cambricon</strong></a> — Targets ~500k AI chips in 2026; Siyuan 590 / next-gen 690 messaging for cloud customers.</li>
+<li><a href="#Biren"><strong>Biren</strong></a> — Hong Kong IPO (6082.HK, Jan 2026); BR100-class GPGPU/AI accelerators.</li>
+<li><a href="#MooreThreads"><strong>Moore Threads</strong></a> — STAR Market IPO Dec 2025; MTT S4000/S5000 AI GPUs with MUSA software stack.</li>
+<li><a href="#MetaX"><strong>MetaX</strong></a> — STAR-listed (688802.SH); C500 in mass production, C600 domestic-process follow-on, MXMACA stack.</li>
+<li><a href="#Hygon"><strong>Hygon</strong></a> — DCU GPGPUs + DTK stack; Day-0 DeepSeek-V4 adaptation alongside Ascend and Cambricon.</li>
+<li><a href="#Iluvatar"><strong>Iluvatar CoreX</strong></a> — HK-listed; multi-gen GPU roadmap (Tianshu→Tianquan) aiming at Hopper–Rubin class gap; BI-V150 deployed.</li>
+<li><a href="#Enflame"><strong>Enflame</strong></a> — CloudBlazer / iGCU GPUs in China intelligent-computing centers; TopsRider LLM software updates.</li>
+<li><a href="#Horizon"><strong>Horizon Robotics</strong></a> — Journey 6 / BPU Nash designed into China L2+ vehicles with stronger Transformer support.</li>
+<li><a href="#BlackSesame"><strong>Black Sesame</strong></a> — HK-listed; Huashan A1000 in mass-production vehicles; A2000 messaging for higher-perf AD.</li>
 </ul>
 
 <div align="center" class="section-title"><h2>Shortcut</h2></div>
@@ -52,12 +91,10 @@
 
 <div align="center" class="section-title"><h2><a name="IC_Vendors"></a>I. IC Vendors</h2></div>
 <hr class="section-rule" />
-
 <a name="Nvidia"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Nvidia_logo.png" height="50" loading="lazy" alt="NVIDIA" /></div>
 <div align="center" class="vendor-title"><h3>NVIDIA — GPU / AI accelerator lineage</h3></div>
-
-<p>Architecture history for AI/HPC (CUDA era → rack-scale platforms). Figures are vendor-reported.</p>
+<p class="vendor-blurb">NVIDIA turned GPUs into the default AI training/inference substrate via CUDA (2007) and successive datacenter architectures. From Pascal/Volta Tensor Cores through Ampere and Hopper, the stack evolved into rack-scale Grace+Blackwell systems; Rubin extends that AI-factory model into 2026.</p>
 
 <p><strong>CUDA foundation (pre–Tensor Core)</strong></p>
 <ul>
@@ -82,115 +119,107 @@
 <li><strong>Blackwell (2024) — B200 / GB200 NVL72</strong> — multi-die GPU, FP4/FP6 datapaths, NVLink 5; Grace+Blackwell superchips and NVL72 racks.</li>
 <li><strong>Rubin (CES/GTC 2026)</strong> — Vera CPU + Rubin GPU + NVLink 6 Switch + ConnectX-9 / BlueField-4 / Spectrum-6 for agentic / MoE; partner availability targeted H2 2026. NVIDIA also described integrating Groq-class LPU technology into the AI-factory stack.</li>
 </ul>
-
-<p><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Rubin platform announcement</a></strong></p>
-<blockquote>
-  <p>CES 2026 — Vera Rubin platform for agentic AI and large MoE models; NVIDIA claims large gains in inference token cost and training GPU-efficiency vs. Blackwell.</p>
-</blockquote>
-
-<p><strong><a href="https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing">NVIDIA Blackwell platform</a></strong></p>
-<blockquote>
-  <p>GTC 2024 — B200 Tensor Core GPU and GB200 Grace Blackwell Superchip / NVL72 systems for generative AI training and real-time LLM inference.</p>
-</blockquote>
-
-<p><a href="https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth">Hopper architecture in-depth</a> ·
-<a href="https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/">Hopper</a> ·
-<a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">Blackwell</a></p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Rubin platform</a></strong> — CES 2026 — Vera Rubin for agentic AI and large MoE; vendor claims large gains in inference token cost and training GPU-efficiency vs. Blackwell; H2 2026 partner availability.</li>
+<li><strong><a href="https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing">Blackwell platform</a></strong> — GTC 2024 — B200 / GB200 Grace Blackwell Superchip and NVL72 systems remain the volume train/serve platform while Rubin ramps.</li>
+</ul>
+<p><a href="https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth">Hopper in-depth</a> · <a href="https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/">Hopper</a> · <a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">Blackwell</a></p>
 
 <a name="Intel"></a>
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Intel_logo.png" height="60" loading="lazy" alt="Intel" /></div>
-<div align="center" class="vendor-title"><h3>Intel / Habana Gaudi</h3></div>
 <a name="Habana"></a>
-<p><strong><a href="https://newsroom.intel.com/artificial-intelligence/vision-2024-enterprise-ai-gaudi-3-open-systems-strategy">Intel Unleashes Enterprise AI with Gaudi 3</a></strong></p>
-<blockquote>
-  <p>Intel Vision 2024 — Gaudi 3 targets generative AI training and inference with Ethernet scale-out. Intel positions Gaudi 3 for enterprise GenAI price/performance vs. GPU alternatives, with OEM availability through Dell, HPE, Lenovo, Supermicro and cloud partners.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Intel_logo.png" height="60" loading="lazy" alt="Intel / Habana" /></div>
+<div align="center" class="vendor-title"><h3>Intel / Habana Gaudi</h3></div>
+<p class="vendor-blurb">Intel’s AI accelerator path consolidated around Habana Labs after the 2019 acquisition. Gaudi (training) and Goya (inference) established Ethernet-centric scale-out as an alternative to proprietary GPU fabrics; Gaudi 2 broadened LLM training, and Gaudi 3 (2024) targets enterprise generative AI with OEM and cloud availability. Intel also folds AI into Xeon AMX and Arc/Flex GPUs, but Gaudi remains the dedicated datacenter accelerator brand.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://newsroom.intel.com/artificial-intelligence/vision-2024-enterprise-ai-gaudi-3-open-systems-strategy">Gaudi 3 for enterprise AI</a></strong> — Intel Vision 2024 — Gaudi 3 for GenAI train/infer with Ethernet scale-out; positioned on price/performance vs. GPU alternatives via Dell, HPE, Lenovo, Supermicro and cloud partners.</li>
+</ul>
+
+<a name="AMD"></a>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AMD_logo.png" height="35" loading="lazy" alt="AMD" /></div>
+<div align="center" class="vendor-title"><h3>AMD — Instinct AI GPUs</h3></div>
+<p class="vendor-blurb">AMD’s datacenter AI story runs through the Instinct line on CDNA architecture (distinct from RDNA gaming GPUs), with ROCm as the software stack. MI100/MI200 established HBM-heavy training SKUs; MI300A/X (CDNA 3, 2023) brought chiplet APU/GPU designs into large LLM clusters. MI350 (CDNA 4, 2025) pushes HBM3E capacity and low-precision datatypes; MI400 / Helios rack systems headline the 2026 roadmap alongside deep hyperscaler partnerships.</p>
+<ul>
+<li><strong>MI100 / MI200 (CDNA 1–2)</strong> — early Instinct training accelerators; MI250X widely used in HPC+AI.</li>
+<li><strong>MI300A / MI300X (2023)</strong> — CDNA 3 chiplets; MI300X became a primary non-NVIDIA LLM training/inference GPU.</li>
+<li><strong>MI350X / MI355X (2025)</strong> — CDNA 4, up to 288GB HBM3E, MXFP6/MXFP4 paths.</li>
+<li><strong>MI400 / Helios (roadmap 2026)</strong> — rack-scale systems and custom SKUs for hyperscalers.</li>
+</ul>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.amd.com/en/products/accelerators/instinct/mi350.html">Instinct MI350 Series</a></strong> — 4th Gen CDNA — MI350X/MI355X with large HBM3E and expanded low-precision support for GenAI train/infer; MI400/Helios on the 2026 roadmap.</li>
+<li><strong><a href="https://www.amd.com/en/newsroom/press-releases/2026-2-24-amd-and-meta-announce-expanded-strategic-partnersh.html">AMD–Meta strategic partnership</a></strong> — Feb 2026 — multi-generation Instinct deployments including custom MI450-class GPUs on Helios with ROCm, targeting multi-gigawatt scale.</li>
+</ul>
 
 <a name="Qualcomm"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Qualcomm_logo.png" height="40" loading="lazy" alt="Qualcomm" /></div>
-
-<p><strong><a href="https://www.androidauthority.com/snapdragon-8-elite-3491805/">Snapdragon 8 Elite — Hexagon NPU for on-device GenAI</a></strong></p>
-<blockquote>
-  <p>Snapdragon Summit 2024 — custom Oryon CPU plus a redesigned Hexagon NPU (fused scalar/vector/tensor accelerators). Qualcomm cites ~45% faster NPU performance and better perf/W vs. Snapdragon 8 Gen 3 for multimodal on-device generative AI.</p>
-</blockquote>
-<p><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Qualcomm Cloud AI 100</a></p>
-<blockquote>
-  <p>Cloud/edge inference accelerator focused on power-efficient deployment outside traditional GPU clusters.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Qualcomm — Hexagon NPU &amp; Cloud AI</h3></div>
+<p class="vendor-blurb">Qualcomm’s AI silicon spans mobile Hexagon NPUs inside Snapdragon SoCs and Cloud AI inference accelerators for power-efficient serving. Hexagon evolved from DSP blocks into fused scalar/vector/tensor NPUs for on-device multimodal GenAI; Cloud AI 100 targets edge/cloud inference outside GPU clusters. Recent flagships (8 Gen 3 → 8 Elite) emphasize agentic and multimodal experiences at the handset.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.androidauthority.com/snapdragon-8-elite-3491805/">Snapdragon 8 Elite — Hexagon NPU</a></strong> — Snapdragon Summit 2024 — Oryon CPU + redesigned Hexagon NPU; Qualcomm cites ~45% faster NPU and better perf/W vs. 8 Gen 3 for on-device GenAI.</li>
+<li><strong><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Cloud AI 100</a></strong> — Cloud/edge inference accelerator focused on power-efficient deployment outside traditional GPU clusters.</li>
+</ul>
 
 <a name="Samsung"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Samsung_logo.png" height="35" loading="lazy" alt="Samsung" /></div>
-<p><strong><a href="https://semiconductor.samsung.com/processor/mobile-processor/exynos-2500/">Samsung Exynos 2500</a></strong></p>
-<blockquote>
-  <p>2025 flagship mobile SoC on Samsung 3nm GAA with a substantially upgraded NPU (Samsung cites up to ~59 TOPS and large on-device AI gains vs. Exynos 2400) for generative AI and privacy-preserving on-device inference.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Samsung — Exynos NPU</h3></div>
+<p class="vendor-blurb">Samsung integrates NPUs into Exynos mobile SoCs and builds memory (HBM) that underpins datacenter AI GPUs. On the compute side, Exynos NPUs have scaled with each flagship generation for vision and on-device generative AI; Exynos 2500 on 3nm GAA is the current high-water mark for Samsung’s own mobile AI silicon.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://semiconductor.samsung.com/processor/mobile-processor/exynos-2500/">Exynos 2500</a></strong> — 2025 flagship on 3nm GAA; Samsung cites up to ~59 TOPS NPU and large on-device AI gains vs. Exynos 2400 for GenAI inference.</li>
+</ul>
 
 <a name="MediaTek"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/MediaTek_logo.png" height="40" loading="lazy" alt="MediaTek" /></div>
-
-<p><strong><a href="https://www.mediatek.com/press-room/mediateks-dimensity-9400-flagship-soc-offers-extreme-performance-and-efficiency-for-the-latest-ai-experiences">MediaTek Dimensity 9400 — 8th Gen NPU for agentic AI</a></strong></p>
-<blockquote>
-  <p>Oct 2024 — Flagship Dimensity 9400 (TSMC 3nm) pairs an all-big-core CPU with MediaTek’s 8th-generation NPU 890. MediaTek highlights on-device LoRA training, high-quality video generation, MoE LLM support, and the Dimensity Agentic AI Engine (DAE), with claimed large gains in LLM prompt / diffusion performance vs. Dimensity 9300.</p>
-</blockquote>
-
-<a name="AMD"></a>
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AMD_logo.png" height="35" loading="lazy" alt="AMD" /></div>
-<p><strong><a href="https://www.amd.com/en/products/accelerators/instinct/mi350.html">AMD Instinct™ MI350 Series GPUs</a></strong></p>
-<blockquote>
-  <p>Built on 4th Gen AMD CDNA™ architecture, MI350X / MI355X deliver large HBM3E capacity (up to 288GB) and high bandwidth for generative AI training and inference, with expanded low-precision datatype support (including MXFP6 / MXFP4). MI400-series / Helios rack-scale systems are on AMD’s 2026 roadmap.</p>
-</blockquote>
-<p><strong><a href="https://www.amd.com/en/newsroom/press-releases/2026-2-24-amd-and-meta-announce-expanded-strategic-partnersh.html">AMD and Meta Announce Expanded Strategic Partnership to Deploy 6 Gigawatts of AMD GPUs</a></strong></p>
-<blockquote>
-  <p>Feb 2026 — AMD and Meta expanded their partnership for multi-generation Instinct GPU deployments, including custom MI450-class GPUs on the Helios rack architecture with ROCm software.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>MediaTek — Dimensity NPU</h3></div>
+<p class="vendor-blurb">MediaTek’s Dimensity flagship line pairs Arm CPUs with successive in-house NPUs for Android on-device AI. The NPU roadmap (through 8th-gen NPU 890 on Dimensity 9400) emphasizes generative and agentic workloads—LoRA adaptation, diffusion video, and MoE LLMs—alongside the Dimensity Agentic AI Engine software path.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.mediatek.com/press-room/mediateks-dimensity-9400-flagship-soc-offers-extreme-performance-and-efficiency-for-the-latest-ai-experiences">Dimensity 9400 — NPU 890</a></strong> — Oct 2024 — TSMC 3nm flagship with 8th-gen NPU; on-device LoRA, video generation, MoE LLMs, and Dimensity Agentic AI Engine vs. Dimensity 9300.</li>
+</ul>
 
 <a name="Marvell"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Marvell_logo.png" height="60" loading="lazy" alt="Marvell" /></div>
-<p><strong><a href="https://www.marvell.com/products/data-processing-units.html">Custom AI ASICs + DPUs</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Beyond OCTEON / ARMADA DPUs, Marvell is a major custom AI accelerator design partner for hyperscalers (widely reported programs include AWS Trainium/Inferentia-class and Microsoft Maia-class silicon), complementing Broadcom’s XPU franchise.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Marvell — custom AI ASICs &amp; DPUs</h3></div>
+<p class="vendor-blurb">Marvell supplies DPUs (OCTEON / ARMADA) and, increasingly, co-designed custom AI accelerators for cloud providers. Alongside Broadcom’s XPU franchise, Marvell is widely reported as a design partner on AWS Trainium/Inferentia-class and Microsoft Maia-class silicon—making it a key “picks and shovels” vendor in the custom-ASIC wave.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.marvell.com/products/data-processing-units.html">Custom AI ASICs + DPUs</a></strong> — 2024–2026 — custom AI accelerator programs for hyperscalers complement OCTEON/ARMADA DPUs and Ethernet/optical connectivity.</li>
+</ul>
 
 <a name="Broadcom"></a>
-<div align="center"><h3>Broadcom</h3></div>
-
-<p><strong><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI and Broadcom announce strategic collaboration for custom AI accelerators</a></strong></p>
-<blockquote>
-  <p>Oct 2025 — Multi-year plan to deploy ~10 GW of OpenAI-designed accelerators co-developed with Broadcom (Ethernet scale-up/scale-out), with rack deployments targeted from H2 2026 through 2029.</p>
-</blockquote>
-<p><strong><a href="https://www.nasdaq.com/press-release/broadcom-announces-extended-partnership-meta-deploy-technology-support-multi">Broadcom extends partnership with Meta on MTIA custom silicon</a></strong></p>
-<blockquote>
-  <p>Apr 2026 — Broadcom’s XPU platform continues as the co-design / implementation backbone for Meta’s multi-generation MTIA accelerators and supporting Ethernet AI networking fabric.</p>
-</blockquote>
-<p><a href="https://www.hpcwire.com/2025/09/09/broadcom-steps-up-to-challenge-nvidia-in-ai-chip-arms-race/">Broadcom custom XPUs for hyperscaler AI</a></p>
-<blockquote>
-  <p>Broadcom co-designs customer-specific AI accelerators (XPUs) and pairs them with Tomahawk / Jericho Ethernet switching — a long-running model that began with Google TPU generations and now spans multiple frontier labs and cloud providers.</p>
-</blockquote>
-
-<div align="center" class="section-title"><h2><a name="Tech_Giants"></a>II. Tech Giants & HPC Vendors</h2></div>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Broadcom_logo.png" height="45" loading="lazy" alt="Broadcom" /></div>
+<div align="center" class="vendor-title"><h3>Broadcom — custom XPUs</h3></div>
+<p class="vendor-blurb">Broadcom co-designs customer-specific AI accelerators (XPUs) and pairs them with Tomahawk/Jericho Ethernet switching. The model began with Google TPU generations and now spans Meta MTIA, OpenAI custom silicon, and other frontier/cloud programs—positioning Broadcom as the primary ASIC implementation partner opposite merchant GPUs.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI–Broadcom strategic collaboration</a></strong> — Oct 2025 — multi-year plan for ~10 GW of OpenAI-designed accelerators with Broadcom; rack deployments targeted H2 2026–2029.</li>
+<li><strong><a href="https://www.nasdaq.com/press-release/broadcom-announces-extended-partnership-meta-deploy-technology-support-multi">Meta MTIA partnership extended</a></strong> — Apr 2026 — XPU platform continues as co-design backbone for multi-generation MTIA and Ethernet AI fabric.</li>
+</ul>
 
+<div align="center" class="section-title"><h2><a name="Tech_Giants"></a>II. Tech Giants &amp; HPC Vendors</h2></div>
 <hr class="section-rule" />
-
 <a name="Google"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Google_logo.png" height="40" loading="lazy" alt="Google" /></div>
 <div align="center" class="vendor-title"><h3>Google — TPU lineage</h3></div>
-
-<p>Cloud TPU generation history (Edge TPU / Pixel Tensor noted as related). Specs are Google-reported.</p>
+<p class="vendor-blurb">Google invented the modern custom-AI-ASIC pattern with TPU v1 (2015) for production inference, then opened Cloud TPU for external training. Each generation scaled systolic MXUs, HBM, and pod interconnect; v4/v5 pods trained many Google foundation models, while Trillium (v6e) and Ironwood (v7) address GenAI train/serve economics. Edge TPU (Coral) and Pixel Tensor are related but separate product lines.</p>
 
 <p><strong>Early generations</strong></p>
 <ul>
-<li><strong>TPU v1 (2015)</strong> — inference-only systolic MXU (INT8) inside Google production services; public details followed ~2016–2017.</li>
+<li><strong>TPU v1 (2015)</strong> — inference-only systolic MXU (INT8) in Google production services.</li>
 <li><strong>TPU v2 (2017)</strong> — training + inference, bfloat16, HBM, first multi-chip pods.</li>
 <li><strong>TPU v3 (2018)</strong> — higher compute, liquid cooling at pod scale.</li>
-<li><strong>TPU v4 (2021)</strong> — denser pods, 3D torus + optical circuit switches; backbone for many Google foundation-model trainings.</li>
+<li><strong>TPU v4 (2021)</strong> — denser pods, 3D torus + optical circuit switches.</li>
 </ul>
 
 <p><strong>Split SKUs &amp; GenAI scale-up</strong></p>
 <ul>
 <li><strong>TPU v5e (2023)</strong> — cost-efficient training/inference at volume.</li>
-<li><strong>TPU v5p (2023)</strong> — high-end large-pod training performance.</li>
-<li><strong>Trillium / TPU v6e (2024)</strong> — larger MXUs; Google cites ~4.7× peak compute/chip vs. v5e, with higher HBM capacity/bandwidth and better energy efficiency.</li>
-<li><strong>Ironwood / TPU v7 (2025)</strong> — inference-leaning generation with native low-precision (FP8-class) paths and very large pod configurations.</li>
+<li><strong>TPU v5p (2023)</strong> — high-end large-pod training.</li>
+<li><strong>Trillium / TPU v6e (2024)</strong> — larger MXUs; Google cites ~4.7× peak compute/chip vs. v5e.</li>
+<li><strong>Ironwood / TPU v7 (2025)</strong> — inference-leaning generation with FP8-class paths and very large pods.</li>
 </ul>
 
 <p><strong>Related Google silicon</strong></p>
@@ -198,427 +227,395 @@
 <li><strong>Edge TPU</strong> — Coral edge inference ASIC.</li>
 <li><strong>Google Tensor (Pixel)</strong> — mobile SoC ML blocks (separate from Cloud TPU).</li>
 </ul>
-
-<p><strong><a href="https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus">Introducing Trillium (TPU v6e)</a></strong></p>
-<blockquote>
-  <p>Sixth-generation Cloud TPU for foundation-model training and serving; Google cites large per-chip compute and efficiency gains vs. TPU v5e.</p>
-</blockquote>
-
-<p><strong><a href="https://docs.cloud.google.com/tpu/docs/release-notes">Ironwood (TPU v7 / TPU7x) availability notes</a></strong></p>
-<blockquote>
-  <p>Seventh-generation Cloud TPU family for large-scale training and inference (LLMs, MoEs, diffusion).</p>
-</blockquote>
-
-<p><a href="https://cloud.google.com/transform/ai-specialized-chips-tpu-history-gen-ai">Google: 10 years of TPU history</a> ·
-<a href="https://docs.cloud.google.com/tpu/docs/v6e">TPU v6e architecture docs</a> ·
-<a href="https://cloud.google.com/tpu">Cloud TPU</a> ·
-<a href="https://cloud.google.com/edge-tpu/">Edge TPU</a></p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus">Trillium (TPU v6e)</a></strong> — Sixth-generation Cloud TPU for foundation-model training and serving; large per-chip compute/efficiency gains vs. v5e.</li>
+<li><strong><a href="https://docs.cloud.google.com/tpu/docs/release-notes">Ironwood (TPU v7) notes</a></strong> — Seventh-generation Cloud TPU family for large-scale training and inference (LLMs, MoEs, diffusion).</li>
+</ul>
+<p><a href="https://cloud.google.com/transform/ai-specialized-chips-tpu-history-gen-ai">10 years of TPU</a> · <a href="https://docs.cloud.google.com/tpu/docs/v6e">v6e docs</a> · <a href="https://cloud.google.com/tpu">Cloud TPU</a> · <a href="https://cloud.google.com/edge-tpu/">Edge TPU</a></p>
 
 <a name="Amazon_AWS"></a>
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Amazon_AWS.png" height="50" loading="lazy" alt="Amazon AWS" /></div>
-<p><strong><a href="https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/">Amazon EC2 Trn2 / Trainium2 UltraServers</a></strong></p>
-<blockquote>
-  <p>2024–2025 — Trainium2-powered Trn2 instances and UltraServers for generative AI training and inference with NeuronLink scale-up; Inferentia2 (Inf2) continues for cost-efficient LLM serving.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Amazon_AWS.png" height="50" loading="lazy" alt="AWS" /></div>
+<div align="center" class="vendor-title"><h3>Amazon AWS — Trainium &amp; Inferentia</h3></div>
+<p class="vendor-blurb">AWS builds first-party AI silicon for EC2: Inferentia for inference and Trainium for training, compiled via the Neuron SDK. Inferentia1/2 lowered LLM serving cost on Inf1/Inf2; Trainium1 and Trainium2 (Trn1/Trn2) plus UltraServers with NeuronLink scale-up target foundation-model training and large inference inside AWS regions—often co-designed with partners such as Marvell/Annapurna.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/">EC2 Trn2 / Trainium2 UltraServers</a></strong> — 2024–2025 — Trainium2 Trn2 instances and UltraServers for GenAI train/infer with NeuronLink; Inf2 remains the cost-efficient serving path.</li>
+</ul>
 
 <a name="Microsoft"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Microsoft_logo.png" height="60" loading="lazy" alt="Microsoft" /></div>
-<p><strong><a href="https://azure.microsoft.com/en-us/blog/azure-maia-for-the-era-of-ai-from-silicon-to-software-to-systems/">Azure Maia for the era of AI</a></strong></p>
-<blockquote>
-  <p>2024 — Microsoft’s first in-house AI accelerator, Azure Maia 100 (5nm, advanced packaging), co-designed for Azure AI / Copilot-class cloud workloads, with a software stack integrating PyTorch and ONNX Runtime.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Microsoft — Azure Maia</h3></div>
+<p class="vendor-blurb">Microsoft entered custom AI silicon with Azure Maia (accelerator) and Cobalt (Arm CPU) to optimize Azure OpenAI / Copilot economics. Maia 100 (5nm, advanced packaging) is co-designed for Azure AI cloud workloads with a PyTorch/ONNX Runtime software path; subsequent Maia generations are expected to deepen rack-level integration with Azure networking and cooling.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://azure.microsoft.com/en-us/blog/azure-maia-for-the-era-of-ai-from-silicon-to-software-to-systems/">Azure Maia for the era of AI</a></strong> — 2024 — first in-house AI accelerator Maia 100 for Azure AI / Copilot-class workloads, with PyTorch and ONNX Runtime integration.</li>
+</ul>
 
 <a name="OpenAI"></a>
-<div align="center"><h3>OpenAI</h3></div>
-
-<p><strong><a href="https://openai.com/index/openai-broadcom-jalapeno-inference-chip/">OpenAI and Broadcom unveil Jalapeño LLM inference chip</a></strong> · <a href="https://techcrunch.com/2026/06/24/openai-unveils-its-first-custom-chip-built-by-broadcom/">TechCrunch coverage</a></p>
-<blockquote>
-  <p>Jun 2026 — OpenAI’s first custom “Intelligence Processor,” Jalapeño, is an inference-optimized ASIC co-developed with Broadcom (networking + silicon implementation) for lower-cost, higher-utilization LLM serving. First step in a multi-generation platform with initial deployments targeted by end of 2026.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/OpenAI_logo.png" height="48" loading="lazy" alt="OpenAI" /></div>
+<div align="center" class="vendor-title"><h3>OpenAI — custom Intelligence Processors</h3></div>
+<p class="vendor-blurb">Long dependent on NVIDIA GPUs (and Azure capacity), OpenAI moved into custom inference silicon to cut serving cost and diversify supply. Jalapeño—co-developed with Broadcom—is the first “Intelligence Processor,” optimized for LLM inference rather than general training, as the opening move in a multi-generation, multi-gigawatt Broadcom collaboration.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://openai.com/index/openai-broadcom-jalapeno-inference-chip/">Jalapeño LLM inference chip</a></strong> — Jun 2026 — first custom inference-optimized ASIC with Broadcom; initial deployments targeted by end of 2026 within a multi-generation platform.</li>
+</ul>
 
 <a name="Anthropic"></a>
-<div align="center"><h3>Anthropic</h3></div>
-
-<p><strong><a href="https://www.anthropic.com/news/google-broadcom-partnership-compute">Anthropic expands partnership with Google and Broadcom for next-generation compute</a></strong></p>
-<blockquote>
-  <p>Apr 2026 — Expanded agreement for multiple gigawatts of next-generation Google TPU capacity (Broadcom SEC filings cite ~3.5 GW), expected online from 2027, on top of prior &gt;1 GW TPU commitments. Anthropic emphasizes a multi-hardware strategy: AWS Trainium (incl. Project Rainier), Google TPUs, and NVIDIA GPUs.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Anthropic_logo.png" height="48" loading="lazy" alt="Anthropic" /></div>
+<div align="center" class="vendor-title"><h3>Anthropic — multi-hardware compute</h3></div>
+<p class="vendor-blurb">Anthropic does not tape out a merchant chip brand; it co-designs capacity across AWS Trainium (Project Rainier), Google TPUs (Broadcom-implemented), and NVIDIA GPUs. The strategy is deliberate multi-sourcing for Claude training and serving at gigawatt scale, with long-term TPU commitments expanding through 2027+.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.anthropic.com/news/google-broadcom-partnership-compute">Google &amp; Broadcom compute partnership</a></strong> — Apr 2026 — expanded multi-GW next-gen TPU capacity (filings cite ~3.5 GW) from 2027, on top of prior &gt;1 GW TPU plus Trainium and NVIDIA.</li>
+</ul>
 
 <a name="Apple"></a>
-
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Apple_logo.png" height="60" loading="lazy" alt="Apple" /></div>
-
-<p><strong><a href="https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/">Apple unleashes M5, the next big leap in AI performance for Apple silicon</a></strong></p>
-<blockquote>
-  <p>Oct 2025 — M5 (3nm) adds Neural Accelerators in each GPU core and a faster 16-core Neural Engine, targeting on-device Apple Intelligence and local AI workloads across MacBook Pro, iPad Pro, and Vision Pro.</p>
-</blockquote>
-<p><strong><a href="https://www.apple.com/newsroom/2026/03/apple-debuts-m5-pro-and-m5-max-to-supercharge-the-most-demanding-pro-workflows/">Apple debuts M5 Pro and M5 Max</a></strong></p>
-<blockquote>
-  <p>Mar 2026 — Fusion Architecture scales GPU AI compute and unified memory bandwidth (up to 128GB / ~614GB/s on M5 Max) for local LLM prompt processing and pro creative AI workflows.</p>
-</blockquote>
-<p><a href="https://www.apple.com/newsroom/2024/05/apple-introduces-m4-chip/">Apple introduces M4 chip</a></p>
-<blockquote>
-  <p>May 2024 — M4 Neural Engine up to ~38 TOPS, with CPU ML accelerators and high-bandwidth unified memory for on-device AI on iPad Pro and later Macs.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Apple — Neural Engine &amp; Apple silicon AI</h3></div>
+<p class="vendor-blurb">Apple’s on-device AI runs on the Neural Engine and, increasingly, GPU Neural Accelerators inside Apple silicon—not discrete datacenter GPUs. From A11’s first Neural Engine through M1–M4, Apple scaled unified-memory ML performance for privacy-preserving inference; M5 (2025) and M5 Pro/Max (2026) explicitly target Apple Intelligence and local LLM/creative AI workflows.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/">Apple M5</a></strong> — Oct 2025 — Neural Accelerators in each GPU core and a faster 16-core Neural Engine for on-device Apple Intelligence.</li>
+<li><strong><a href="https://www.apple.com/newsroom/2026/03/apple-debuts-m5-pro-and-m5-max-to-supercharge-the-most-demanding-pro-workflows/">M5 Pro and M5 Max</a></strong> — Mar 2026 — Fusion Architecture scales GPU AI compute and unified memory (up to 128GB / ~614GB/s on M5 Max) for local LLM and pro creative AI.</li>
+</ul>
 
 <a name="Alibaba"></a>
-
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/alibaba_logo.png" height="60" loading="lazy" alt="Alibaba" /></div>
-
-<p><strong><a href="https://www.eetimes.com/alibaba-unveils-own-ai-chip-mounting-direct-challenge-to-nvidia/">Alibaba Unveils Own AI Chip, Mounting Direct Challenge to Nvidia</a></strong></p>
-<blockquote>
-  <p>Sep 2025 — Alibaba’s T-Head Parallel Processing Unit (PPU / Zhenwu family) targets cloud AI training and inference as a domestic alternative under export-control constraints. Reports cite ~96GB HBM2e-class memory and high chip-to-chip bandwidth; deployed in large China Unicom / Alibaba Cloud clusters.</p>
-</blockquote>
-
-<p>
+<div align="center" class="vendor-title"><h3>Alibaba — T-Head PPU</h3></div>
+<p class="vendor-blurb">Alibaba’s T-Head unit designs cloud AI accelerators for Alibaba Cloud under the PPU / Zhenwu family, aiming at domestic training and inference as GPU import constraints tighten. Reported SKUs emphasize large HBM capacity and chip-to-chip bandwidth for LLM clusters in China cloud and carrier deployments.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.eetimes.com/alibaba-unveils-own-ai-chip-mounting-direct-challenge-to-nvidia/">Alibaba unveils own AI chip</a></strong> — Sep 2025 — PPU / Zhenwu targets cloud train/infer; reports cite ~96GB HBM2e-class memory and large China Unicom / Alibaba Cloud deployments.</li>
+</ul>
 
 <a name="Baidu"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Baidu_logo.png" height="40" loading="lazy" alt="Baidu" /></div>
-<p><strong><a href="https://www.baidu.com/">Baidu Kunlunxin</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Kunlunxin P800-class GPUs and follow-ons remain a core China training/inference option in Baidu Cloud / ERNIE deployments, with continued software stack updates for large-model serving.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Baidu — Kunlunxin</h3></div>
+<p class="vendor-blurb">Baidu’s Kunlunxin (昆仑芯) GPUs underpin ERNIE and Baidu Cloud AI services. From earlier Kunlun generations through P800-class training/inference cards, Baidu has iterated a full stack (hardware + software) as a China-domestic alternative for large-model workloads.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.baidu.com/">Kunlunxin P800-class</a></strong> — 2024–2026 — P800-class and follow-ons remain a core China train/infer option in Baidu Cloud / ERNIE deployments with ongoing software updates.</li>
+</ul>
 
 <a name="Tencent_Cloud"></a>
-<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tencent_Cloud_logo.png" height="30" loading="lazy" alt="Tencent Cloud" /></div>
-<p><strong>Tencent Cloud AI accelerators (Zixiao / custom)</strong></p>
-<blockquote>
-  <p>2024–2026 — Tencent continues deploying proprietary and partner AI accelerators across Tencent Cloud for recommendation, multimodal, and LLM inference/training inside its China cloud footprint.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tencent_Cloud_logo.png" height="30" loading="lazy" alt="Tencent" /></div>
+<div align="center" class="vendor-title"><h3>Tencent Cloud — Zixiao / custom AI</h3></div>
+<p class="vendor-blurb">Tencent deploys proprietary Zixiao-class and partner AI accelerators across Tencent Cloud for recommendation, multimodal, and LLM train/infer inside its China footprint—part of a broader mix that also includes merchant GPUs where available.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong>Tencent Cloud AI accelerators</strong> — 2024–2026 — continued proprietary and partner accelerator deployments for recommendation, multimodal, and LLM workloads on Tencent Cloud.</li>
+</ul>
 
 <a name="Facebook"></a>
-
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/facebook_logo.png" height="50" loading="lazy" alt="Meta" /></div>
-
-<p><strong><a href="https://ai.meta.com/blog/meta-mtia-scale-ai-chips-for-billions/">Four MTIA Chips in Two Years: Scaling AI Experiences for Billions</a></strong></p>
-<blockquote>
-  <p>Meta describes a multi-generation MTIA roadmap (including MTIA 300–500 class chips) with modular chiplet designs and accelerating deployment cadence through 2026–2027.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Meta — MTIA</h3></div>
+<p class="vendor-blurb">Meta’s MTIA (Meta Training and Inference Accelerator) is an in-house family for ranking, recommendation, and increasingly generative workloads at hyperscale. Built with Broadcom as implementation partner, MTIA has moved to a rapid multi-generation chiplet cadence (MTIA 300–500 class messaging) aimed at Meta’s production AI fleet rather than merchant sale.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://ai.meta.com/blog/meta-mtia-scale-ai-chips-for-billions/">Four MTIA chips in two years</a></strong> — Multi-generation MTIA roadmap with modular chiplets and accelerating deployment cadence through 2026–2027.</li>
+</ul>
 
 <a name="Tesla"></a>
-
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tesla_logo.png" height="60" loading="lazy" alt="Tesla" /></div>
-
-<p><strong><a href="https://electrek.co/2026/04/15/tesla-ai5-chip-taped-out-musk-ai6-dojo3/">Tesla AI5 chip taped out</a></strong></p>
-<blockquote>
-  <p>Apr 2026 — Elon Musk announced Tesla’s next-generation AI5 inference/training SoC has taped out, with AI6 / Dojo3 also mentioned as in progress. Volume production and broad vehicle deployment remain gated (reports point to mid–late 2027+); near-term vehicles continue on AI4 / interim upgrades, while AI5 is also positioned for Optimus and internal clusters.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Tesla — HW / AI inference &amp; Dojo</h3></div>
+<p class="vendor-blurb">Tesla designs FSD/AI inference SoCs for vehicles (HW3 → HW4/AI4 → AI5) and pursued Dojo training tiles for internal vision-model training. The vertical stack ties cars, Optimus, and data-center inference together; AI5 is the next major SoC step after AI4, with Dojo3 still on the longer training roadmap.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://electrek.co/2026/04/15/tesla-ai5-chip-taped-out-musk-ai6-dojo3/">Tesla AI5 taped out</a></strong> — Apr 2026 — AI5 inference/training SoC taped out; AI6/Dojo3 mentioned as in progress; broad vehicle deployment still gated (reports: mid–late 2027+).</li>
+</ul>
 
 <div align="center" class="section-title"><h2><a name="IP_Vendors"></a>III. Traditional IP Vendors</h2></div>
-
 <hr class="section-rule" />
-
 <a name="ARM"></a>
-
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/ARM_logo.png" height="30" loading="lazy" alt="Arm" /></div>
-
-<p><strong><a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85">Arm Ethos-U85</a></strong> — third-generation Ethos-U micro-NPU</p>
-<blockquote>
-  <p>Scales from 128–2048 MACs (up to ~4 TOPS @ 1 GHz) with native transformer operator support for edge / IoT generative and vision models; ~20% more energy efficient than Ethos-U65 and pairs with Cortex-M or Cortex-A hosts (Corstone-320 reference). Same Ethos toolchain / Vela compiler path as prior U-series.</p>
-</blockquote>
-
-<p><a href="https://newsroom.arm.com/news/iot-reference-design-platform-2024">Arm newsroom: Ethos-U85 + Corstone-320 IoT reference platform</a></p>
-
-<p>
+<div align="center" class="vendor-title"><h3>Arm — Ethos NPU IP</h3></div>
+<p class="vendor-blurb">Arm licenses Ethos NPU IP alongside Cortex CPUs for edge and IoT SoCs. The Ethos-U micro-NPU line (U55 → U65 → U85) scales MAC arrays and adds transformer operators while keeping the Vela/Ethos toolchain path; larger Ethos-N products address higher-TOPS vision/GenAI in Cortex-A systems.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85">Ethos-U85</a></strong> — Third-gen Ethos-U (128–2048 MACs, up to ~4 TOPS @ 1 GHz) with native transformer support; pairs with Cortex-M/A and Corstone-320.</li>
+</ul>
 
 <a name="Synopsys"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Synopsys_logo.png" height="40" loading="lazy" alt="Synopsys" /></div>
-<p><strong><a href="https://www.synopsys.com/designware-ip/processor-solutions/neural-processing-unit.html">Synopsys ARC NPX NPU IP</a></strong></p>
-<blockquote>
-  <p>2024–2026 — DesignWare ARC NPX neural processor IP family for automotive / consumer / datacenter SoCs, with multi-TOPS scalable configurations and ongoing toolchain updates.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Synopsys — ARC NPX NPU IP</h3></div>
+<p class="vendor-blurb">Synopsys DesignWare ARC NPX provides licensable neural processor IP for SoC integrators across automotive, consumer, and infrastructure. The family emphasizes scalable TOPS, functional-safety options, and toolchain continuity for DNN/transformer graphs.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.synopsys.com/designware-ip/processor-solutions/neural-processing-unit.html">ARC NPX NPU IP</a></strong> — 2024–2026 — scalable NPX configurations with ongoing toolchain updates for modern DNN / transformer workloads.</li>
+</ul>
 
 <a name="Imagination"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Imagination_logo.png" height="60" loading="lazy" alt="Imagination" /></div>
-<p><strong><a href="https://www.imaginationtech.com/products/ai/">Imagination AI / NNA IP</a></strong></p>
-<blockquote>
-  <p>2024–2026 — PowerVR Neural Network Accelerators (NNA) and GPU+NNA combinations for edge and automotive SoCs, with IMG Continuum software for transformer-class networks.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Imagination — NNA IP</h3></div>
+<p class="vendor-blurb">Imagination’s PowerVR Neural Network Accelerators (NNA) and GPU+NNA combinations target edge and automotive SoCs. IMG Continuum software maps CNN and transformer-class networks onto the NNA datapaths for licensees.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.imaginationtech.com/products/ai/">Imagination AI / NNA IP</a></strong> — 2024–2026 — PowerVR NNA and GPU+NNA IP with Continuum for edge and automotive transformer-class networks.</li>
+</ul>
 
 <a name="CEVA"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/CEVA_logo.png" height="40" loading="lazy" alt="CEVA" /></div>
-<p><strong><a href="https://www.ceva-dsp.com/app/deep-learning/">CEVA edge AI / NPU IP</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Low-power AI processor IP and DNN graph compiler for real-time embedded / automotive / IoT inference, including transformer-capable NeuPro-class offerings.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>CEVA — NeuPro / edge AI IP</h3></div>
+<p class="vendor-blurb">CEVA licenses low-power AI processor IP and DSP/NPU hybrids for real-time embedded, automotive, and IoT inference. NeuPro-class offerings and the CEVA DNN compiler add transformer-capable paths for on-device GenAI and vision.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.ceva-dsp.com/app/deep-learning/">CEVA edge AI / NPU IP</a></strong> — 2024–2026 — low-power AI IP and graph compiler including transformer-capable NeuPro-class products.</li>
+</ul>
 
 <a name="Cadence"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cadence_logo.png" height="40" loading="lazy" alt="Cadence" /></div>
-<p><strong><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Cadence Tensilica AI Platform</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Tensilica NPU / AI DSP IP for on-device and automotive SoCs, with NeuroWeave toolchain updates for modern DNN / transformer workloads.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Cadence — Tensilica AI IP</h3></div>
+<p class="vendor-blurb">Cadence Tensilica offers configurable NPU and AI DSP IP for on-device and automotive SoCs. The NeuroWeave toolchain maps modern DNN and transformer graphs onto Tensilica datapaths used widely in vision, audio, and edge GenAI chips.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Tensilica AI Platform</a></strong> — 2024–2026 — Tensilica NPU/AI DSP IP with NeuroWeave updates for DNN / transformer workloads.</li>
+</ul>
 
 <a name="VeriSilicon"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/VeriSilicon_logo.png" height="40" loading="lazy" alt="VeriSilicon" /></div>
-<p><strong><a href="https://www.verisilicon.com/">VeriSilicon VIP / NPU IP &amp; design services</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Acuity / VIP NPU IP and turnkey design services widely used in China and global edge SoCs for vision and on-device GenAI.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>VeriSilicon — VIP NPU IP</h3></div>
+<p class="vendor-blurb">VeriSilicon provides VIP/Acuity NPU IP plus turnkey ASIC design services, especially strong in China edge and vision SoCs. The IP covers CNN through on-device GenAI use cases for consumer, automotive, and IoT customers.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.verisilicon.com/">VIP / NPU IP &amp; design services</a></strong> — 2024–2026 — Acuity/VIP NPU IP and turnkey services for vision and on-device GenAI SoCs.</li>
+</ul>
 
 <div align="center" class="section-title"><h2><a name="Startups"></a>IV. Startups</h2></div>
-
 <hr class="section-rule" />
-
 <a name="Cerebras"></a>
-
 <div align="center" class="vendor-logo"><a href="https://www.cerebras.net/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cerebras_logo.png" height="50" loading="lazy" alt="Cerebras" /></a></div>
-
-<p><strong><a href="https://www.cerebras.net/press-release/cerebras-announces-third-generation-wafer-scale-engine">Cerebras Systems Unveils World’s Fastest AI Chip with 4 Trillion Transistors (WSE-3)</a></strong></p>
-<blockquote>
-  <p>March 2024 — WSE-3 (5nm) powers the CS-3 system with ~900,000 AI-optimized cores and 125 petaflops peak AI performance, doubling WSE-2 performance at the same power/price point for large-model training and high-throughput inference.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Cerebras — wafer-scale engine</h3></div>
+<p class="vendor-blurb">Cerebras builds wafer-scale AI processors (WSE) that place an enormous on-wafer SRAM/compute fabric into CS systems, avoiding many multi-GPU communication bottlenecks. WSE-1/2 established the approach; WSE-3 (2024, 5nm) powers CS-3 for foundation-model training and high-throughput inference with cloud and on-prem deployments.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.cerebras.net/press-release/cerebras-announces-third-generation-wafer-scale-engine">WSE-3 announcement</a></strong> — Mar 2024 — WSE-3 (~4T transistors, ~900k AI cores, 125 petaflops peak AI) doubles WSE-2 performance at similar power/price for CS-3.</li>
+</ul>
 
 <a name="Graphcore"></a>
-
 <div align="center" class="vendor-logo"><a href="https://www.graphcore.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Graphcore_logo.png" height="70" loading="lazy" alt="Graphcore" /></a></div>
-
-<p><strong><a href="https://www.graphcore.ai/posts/graphcore-joins-softbank-group-to-build-next-generation-of-ai-compute">Graphcore joins SoftBank Group to build next generation of AI compute</a></strong></p>
-<blockquote>
-  <p>Jul 2024 — SoftBank acquired Graphcore as a wholly owned subsidiary (terms undisclosed; press estimates ~$500M). Graphcore continues under its own brand, with SoftBank backing next-generation IPU / AI compute development (including SoftBank-ecosystem follow-on investment reporting into 2026).</p>
-</blockquote>
-
-<p>
-
-<p>
+<div align="center" class="vendor-title"><h3>Graphcore — IPU</h3></div>
+<p class="vendor-blurb">Graphcore pioneered Intelligence Processing Units (IPU) with fine-grained MIMD parallelism and large on-chip memory (Colossus Mk1/Mk2, Bow). After competing in the pre-LLM and early-LLM era, SoftBank acquired Graphcore (2024) as a wholly owned subsidiary to fund next-generation AI compute while keeping the Graphcore brand.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.graphcore.ai/posts/graphcore-joins-softbank-group-to-build-next-generation-of-ai-compute">Graphcore joins SoftBank Group</a></strong> — Jul 2024 — SoftBank acquisition (terms undisclosed; press estimates ~$500M); Graphcore continues under its brand with SoftBank-backed next-gen development.</li>
+</ul>
 
 <a name="Tenstorrent"></a>
 <div align="center" class="vendor-logo"><a href="http://tenstorrent.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Tenstorrent_logo.png" height="80" loading="lazy" alt="Tenstorrent" /></a></div>
-<p><strong><a href="https://tenstorrent.com/">Tenstorrent Wormhole / Blackhole</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Tensix-based AI processors (Wormhole shipping; Blackhole next) with open software (TT-Metalium) and growing rack-scale deployments; Jim Keller–led roadmap emphasizes scalable chiplet meshes.</p>
-</blockquote>
-
-<a name="Blaize"></a>
-<div align="center" class="vendor-logo"><a href="https://www.blaize.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blaize_logo.png" height="40" loading="lazy" alt="Blaize" /></a></div>
-<p><strong><a href="https://www.blaize.com/">Blaize AI edge platforms (Nasdaq: BZAI)</a></strong></p>
-<blockquote>
-  <p>2025 — Listed via SPAC; Graph Streaming Processor (GSP) architecture for edge AI vision / GenAI inference in industrial and smart-city deployments.</p>
-</blockquote>
-
-<a name="Mythic"></a>
-
-<div align="center" class="vendor-logo"><a href="https://mythic.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Mythic_logo.png" height="20" loading="lazy" alt="Mythic" /></a></div>
-
-<p><strong><a href="https://www.businesswire.com/news/home/20251217546249/en/Mythic-to-Challenge-AIs-GPU-Pantheon-with-100x-Energy-Advantage-and-Oversubscribed-%24125M-Raise">Mythic raises $125M Series D for analog AI compute</a></strong></p>
-<blockquote>
-  <p>Dec 2025 — Oversubscribed round led by DCVC (strategic participation including Honda and Lockheed Martin) to scale Mythic’s Analog Processing Units (APUs) — flash-based compute-in-memory for energy-efficient inference at the edge and in data centers. Roadmap emphasizes chiplet-based next-gen APUs for robotics, automotive, and defense.</p>
-</blockquote>
-<p><strong><a href="https://mythic.ai/whats-new/mythic-acquires-videantis-one-of-europes-leading-digital-processor-ip-companies-to-build-the-worlds-most-energy-efficient-ai-compute-platform/">Mythic acquires Videantis for hybrid analog/digital AI platform</a></strong></p>
-<blockquote>
-  <p>May 2026 — Acquisition of Videantis GmbH adds a production digital processor IP + software stack alongside Mythic’s analog in-memory compute, targeting a hybrid platform with large claimed efficiency gains vs. conventional GPU inference.</p>
-</blockquote>
-
-<p>
+<div align="center" class="vendor-title"><h3>Tenstorrent — Wormhole / Blackhole</h3></div>
+<p class="vendor-blurb">Tenstorrent (Jim Keller–led) builds Tensix-based AI processors as scalable chiplet meshes with an open software stack (TT-Metalium). Wormhole-generation cards are shipping into developer and rack deployments; Blackhole is the next architecture step toward higher-density train/infer systems.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://tenstorrent.com/">Wormhole / Blackhole</a></strong> — 2024–2026 — Wormhole shipping; Blackhole next; open TT-Metalium and growing rack-scale deployments on chiplet meshes.</li>
+</ul>
 
-<a name="Kinara"></a>
-<div align="center" class="vendor-logo"><a href="https://www.kinara.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Deepvision_logo.png" height="40" loading="lazy" alt="Kinara" /></a></div>
-<div align="center" class="vendor-title"><h3>Kinara (ex–Deep Vision)</h3></div>
-<p><strong><a href="https://www.nxp.com/company/about-nxp/newsroom/NW-NXP-TO-ACQUIRE-KINARA">NXP to acquire Kinara (2025)</a></strong></p>
-<blockquote>
-  <p>Feb 2025 — NXP announced acquisition of Kinara (rebranded from Deep Vision) for edge AI / Ara / Prisma-class NPUs used in vision and on-device GenAI inference; expands NXP’s industrial and automotive edge AI portfolio.</p>
-</blockquote>
+<a name="Etched"></a>
+<div align="center" class="vendor-logo"><a href="https://www.etched.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Etched_logo.png" height="48" loading="lazy" alt="Etched" /></a></div>
+<div align="center" class="vendor-title"><h3>Etched — transformer ASICs</h3></div>
+<p class="vendor-blurb">Etched builds ASICs purpose-built for Transformer inference (Sohu) rather than general-purpose GPU programmability—betting that specialization wins tokens/sec and energy efficiency for LLM serving. TSMC-manufactured silicon and “frontier inference cluster” messaging mark a fast rise from 2024 Series A through 2026 scale-up.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://techcrunch.com/2026/06/30/nvidia-competitor-etched-hits-5b-valuation-1b-in-sales-for-ai-chip/">Etched hits $5B valuation</a></strong> — Jun 2026 — ~$800M raised to date (incl. $500M at $5B valuation) and ~$1B in booked system orders for transformer-specialized ASICs.</li>
+</ul>
 
 <a name="Groq"></a>
-
-<div align="center" class="vendor-title"><h3><a href="https://groq.com/">Groq</a></h3></div>
-
-<p><strong><a href="https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Vera-Rubin-Opens-Agentic-AI-Frontier/default.aspx">NVIDIA Vera Rubin Opens Agentic AI Frontier (includes Groq LPU)</a></strong></p>
-<blockquote>
-  <p>In late 2025 NVIDIA licensed Groq’s LPU technology and brought on key engineering talent. At GTC 2026, NVIDIA described integrating Groq-class LPU engines into the Vera Rubin AI factory stack for low-latency / high-efficiency token generation alongside Rubin GPUs.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><a href="https://groq.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Groq_logo.png" height="48" loading="lazy" alt="Groq" /></a></div>
+<div align="center" class="vendor-title"><h3>Groq — LPU</h3></div>
+<p class="vendor-blurb">Groq’s Language Processing Unit (LPU) uses a deterministic, compiler-scheduled architecture for ultra-low-latency LLM token generation. After building a cloud inference service on LPU racks, Groq’s technology was licensed by NVIDIA (late 2025), with key talent joining NVIDIA; GTC 2026 described LPU-class engines inside the Vera Rubin AI factory for efficient decode alongside Rubin GPUs.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Vera-Rubin-Opens-Agentic-AI-Frontier/default.aspx">NVIDIA Vera Rubin includes Groq LPU</a></strong> — Late 2025 NVIDIA licensed Groq LPU tech; GTC 2026 — LPU-class engines integrated into Vera Rubin for low-latency / high-efficiency token generation.</li>
+</ul>
 
 <a name="SambaNova"></a>
-
 <div align="center" class="vendor-logo"><a href="https://sambanova.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SambaNova_logo.png" height="40" loading="lazy" alt="SambaNova" /></a></div>
+<div align="center" class="vendor-title"><h3>SambaNova — RDU</h3></div>
+<p class="vendor-blurb">SambaNova’s Reconfigurable Dataflow Unit (RDU) maps full model graphs onto a dataflow fabric for enterprise and agentic inference. SN40L established the GenAI-era platform; SN50 (5th gen) scales compute and network bandwidth for large agentic deployments with SambaNova’s full-stack software.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://sambanova.ai/press/sambanova-unveils-fastest-chip-for-agentic-ai-collaborates-with-intel-and-raises-350m">SN50 RDU, Intel collab, $350M+ Series E</a></strong> — Feb 2026 — SN50 targets agentic inference (vendor: ~5× compute / ~4× network vs. SN40L); SoftBank early deploy; H2 2026 shipping; Intel heterogeneous-inference collaboration.</li>
+</ul>
 
-<p><strong><a href="https://sambanova.ai/press/sambanova-unveils-fastest-chip-for-agentic-ai-collaborates-with-intel-and-raises-350m">SambaNova unveils SN50 RDU for agentic AI, Intel collaboration, $350M+ Series E</a></strong></p>
-<blockquote>
-  <p>Feb 2026 — Fifth-generation Reconfigurable Dataflow Unit (SN50) targets large-scale agentic inference (vendor claims up to ~5× compute and ~4× network bandwidth vs. SN40L; scales toward 256 accelerators). SoftBank named as an early deploy partner; shipping targeted for H2 2026. Planned multi-year collaboration with Intel for heterogeneous inference stacks; oversubscribed Series E led with Vista / Cambium participation including Intel Capital.</p>
-</blockquote>
-
-<p>
-
-<a name="SynSense"></a>
-<div align="center" class="vendor-logo"><a href="https://www.synsense.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/aiCTX_logo.png" height="40" loading="lazy" alt="SynSense" /></a></div>
-<div align="center" class="vendor-title"><h3>SynSense (ex–aiCTX)</h3></div>
-<p><strong><a href="https://www.synsense.ai/">SynSense neuromorphic / event-based sensing+compute</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Continues Speck / Xylo-class neuromorphic chips; 2024 iniVation collaboration and subsequent funding extend event-based vision + edge AI offerings.</p>
-</blockquote>
+<a name="Mythic"></a>
+<div align="center" class="vendor-logo"><a href="https://mythic.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Mythic_logo.png" height="20" loading="lazy" alt="Mythic" /></a></div>
+<div align="center" class="vendor-title"><h3>Mythic — analog compute-in-memory</h3></div>
+<p class="vendor-blurb">Mythic builds Analog Processing Units (APUs) that use flash-based compute-in-memory for energy-efficient inference at the edge and in data centers. After years of analog AI development, 2025–2026 funding and the Videantis acquisition add digital processor IP for a hybrid analog/digital platform aimed at robotics, automotive, and defense.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.businesswire.com/news/home/20251217546249/en/Mythic-to-Challenge-AIs-GPU-Pantheon-with-100x-Energy-Advantage-and-Oversubscribed-%24125M-Raise">$125M Series D</a></strong> — Dec 2025 — oversubscribed DCVC-led round (Honda, Lockheed Martin among strategics) to scale APUs and chiplet next-gen products.</li>
+<li><strong><a href="https://mythic.ai/whats-new/mythic-acquires-videantis-one-of-europes-leading-digital-processor-ip-companies-to-build-the-worlds-most-energy-efficient-ai-compute-platform/">Acquires Videantis</a></strong> — May 2026 — Videantis digital processor IP + software joins Mythic analog CIM for a hybrid efficiency-focused platform.</li>
+</ul>
 
 <a name="Hailo"></a>
-
 <div align="center" class="vendor-logo"><a href="https://hailo.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Hailo_logo.png" height="60" loading="lazy" alt="Hailo" /></a></div>
+<div align="center" class="vendor-title"><h3>Hailo — edge AI accelerators</h3></div>
+<p class="vendor-blurb">Hailo designs high-efficiency edge AI accelerators for vision and, with Hailo-10, on-device generative AI. Hailo-8 established the structure-sparsity / efficient-NPU brand in smart cameras and industrial vision; Hailo-10H adds LLM/VLM-class GenAI at low watts with automotive qualification paths.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://hailo.ai/company-overview/newsroom/news/hailo-announces-general-availability-of-hailo-10h-edge-ai-accelerator-with-generative-ai-capabilities/">Hailo-10H GA for edge GenAI</a></strong> — Jul 2025 — second-gen Hailo-10H for on-device LLM/VLM/GenAI (~40 TOPS INT4, ~2.5W typical); AEC-Q100 Grade 2 with 2026 SOP targeting.</li>
+</ul>
 
-<p><strong><a href="https://hailo.ai/company-overview/newsroom/news/hailo-announces-general-availability-of-hailo-10h-edge-ai-accelerator-with-generative-ai-capabilities/">Hailo-10H generally available for edge generative AI</a></strong></p>
-<blockquote>
-  <p>Jul 2025 — Second-generation Hailo-10H brings on-device LLM / VLM / GenAI capabilities to the edge (~40 TOPS INT4 class, ~2.5W typical), while retaining vision workloads from the Hailo-8 generation. Automotive-qualified (AEC-Q100 Grade 2) with 2026 SOP targeting for some designs.</p>
-</blockquote>
-
-<p>
-
-<p>
-
-<p>
-
-<p>
-
-<p>
-
-<p>
-
-<p>
+<a name="Furiosa"></a>
+<div align="center" class="vendor-logo"><a href="https://www.furiosa.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Furiosa_logo.png" height="60" loading="lazy" alt="FuriosaAI" /></a></div>
+<div align="center" class="vendor-title"><h3>FuriosaAI — RNGD / TCP</h3></div>
+<p class="vendor-blurb">Korea-based FuriosaAI builds Tensor Contraction Processor (TCP) accelerators for datacenter inference. After earlier Warboy edge/vision chips, RNGD (TSMC 5nm) targets LLM and agentic serving in air-cooled multi-card servers, with a Broadcom collaboration to scale into multi-die Ethernet/PCIe platforms.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://furiosa.ai/blog/rngd-enters-mass-production-the-high-performance-ai-accelerator-for-any-data-center">RNGD mass production</a></strong> — Jan 2026 — RNGD (~180W PCIe) volume shipment for LLM/agentic inference; NXT RNGD servers pack multiple cards into ~3kW-class air-cooled systems.</li>
+<li><strong><a href="https://furiosa.ai/blog/furiosaai-partners-with-broadcom-to-build-next-generation-inference-platform-for-the-agentic-era">Broadcom inference partnership</a></strong> — May 2026 — evolve TCP into a scale-up multi-die inference platform with Broadcom Ethernet/PCIe switching.</li>
+</ul>
 
-<p>
+<a name="Kinara"></a>
+<div align="center" class="vendor-logo"><a href="https://www.kinara.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Deepvision_logo.png" height="40" loading="lazy" alt="Kinara" /></a></div>
+<div align="center" class="vendor-title"><h3>Kinara (ex–Deep Vision)</h3></div>
+<p class="vendor-blurb">Kinara (formerly Deep Vision) builds Ara/Prisma-class edge NPUs for vision and on-device GenAI inference. The company rebranded and scaled edge AI SKUs before NXP announced an acquisition (2025) to fold Kinara into NXP’s industrial and automotive edge AI portfolio.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.nxp.com/company/about-nxp/newsroom/NW-NXP-TO-ACQUIRE-KINARA">NXP to acquire Kinara</a></strong> — Feb 2025 — NXP acquisition of Kinara for edge AI NPUs used in vision and on-device GenAI; expands NXP industrial/automotive AI.</li>
+</ul>
 
 <a name="SiMa"></a>
-
 <div align="center" class="vendor-logo"><a href="https://sima.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/SiMa_logo.png" height="40" loading="lazy" alt="SiMa.ai" /></a></div>
+<div align="center" class="vendor-title"><h3>SiMa.ai — Modalix MLSoC</h3></div>
+<p class="vendor-blurb">SiMa.ai builds MLSoC platforms for edge / Physical AI—robots, industrial, automotive, and vision—combining CPU, ML accelerator, and software (Palette / LLiMa) for CNN through Transformer/LLM workloads at low power. Modalix is the second-generation production platform after the first MLSoC.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://sima.ai/press-release/sima-ai-next-gen-platform-for-physical-ai-in-production/">Modalix platform in production</a></strong> — Aug 2025 — Modalix ships for Physical AI under ~10W with SoM/devkits and LLiMa for on-device LLM deployment.</li>
+</ul>
 
-<p><strong><a href="https://sima.ai/press-release/sima-ai-next-gen-platform-for-physical-ai-in-production/">SiMa.ai Modalix platform for Physical AI in production</a></strong></p>
-<blockquote>
-  <p>Aug 2025 — Second-generation MLSoC Modalix ships for edge / Physical AI (robots, industrial, automotive, vision), targeting CNNs + Transformers/LLMs/GenAI under ~10W, with SoM/devkits and the LLiMa framework for on-device LLM deployment.</p>
-</blockquote>
-
-<p>
-
-<a name="Furiosa"></a>
+<a name="d-matrix"></a>
+<div align="center" class="vendor-logo"><a href="https://www.d-matrix.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/d-matrix_logo.png" height="60" loading="lazy" alt="d-Matrix" /></a></div>
+<div align="center" class="vendor-title"><h3>d-Matrix — Corsair</h3></div>
+<p class="vendor-blurb">d-Matrix builds digital compute-in-memory accelerators (Corsair) for low-latency, high-efficiency datacenter LLM inference—contrasting analog CIM approaches with a digital datapath while still attacking the memory-wall bottleneck that dominates decode.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.d-matrix.ai/">Corsair inference accelerators</a></strong> — 2024–2026 — Corsair for datacenter LLM inference with expanding software and cloud-partner footprint.</li>
+</ul>
 
-<div align="center" class="vendor-logo"><a href="https://www.furiosa.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Furiosa_logo.png" height="60" loading="lazy" alt="FuriosaAI" /></a></div>
+<a name="Blaize"></a>
+<div align="center" class="vendor-logo"><a href="https://www.blaize.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blaize_logo.png" height="40" loading="lazy" alt="Blaize" /></a></div>
+<div align="center" class="vendor-title"><h3>Blaize — Graph Streaming Processor</h3></div>
+<p class="vendor-blurb">Blaize’s Graph Streaming Processor (GSP) targets graph-native edge AI for vision and GenAI in industrial and smart-city deployments. After years as a private edge-AI vendor, Blaize listed via SPAC (Nasdaq: BZAI) in 2025 to scale product and go-to-market.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.blaize.com/">Blaize AI edge platforms</a></strong> — 2025 — listed via SPAC; GSP architecture for edge AI vision / GenAI in industrial and smart-city deployments.</li>
+</ul>
 
-<p><strong><a href="https://furiosa.ai/blog/rngd-enters-mass-production-the-high-performance-ai-accelerator-for-any-data-center">RNGD enters mass production for data-center AI inference</a></strong></p>
-<blockquote>
-  <p>Jan 2026 — FuriosaAI’s RNGD (Tensor Contraction Processor, TSMC 5nm, ~180W PCIe) entered volume shipment for LLM / agentic inference; NXT RNGD servers pack multiple cards into air-cooled, ~3kW-class systems.</p>
-</blockquote>
-<p><strong><a href="https://furiosa.ai/blog/furiosaai-partners-with-broadcom-to-build-next-generation-inference-platform-for-the-agentic-era">FuriosaAI partners with Broadcom on next-generation inference platform</a></strong></p>
-<blockquote>
-  <p>May 2026 — Strategic collaboration to evolve TCP into a scale-up multi-die inference platform with Broadcom Ethernet/PCIe switching for hyperscale agentic workloads.</p>
-</blockquote>
+<a name="SynSense"></a>
+<div align="center" class="vendor-logo"><a href="https://www.synsense.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/aiCTX_logo.png" height="40" loading="lazy" alt="SynSense" /></a></div>
+<div align="center" class="vendor-title"><h3>SynSense (ex–aiCTX)</h3></div>
+<p class="vendor-blurb">SynSense (formerly aiCTX) builds neuromorphic and event-based sensing+compute chips (Speck, Xylo) for ultra-low-power always-on perception. Collaborations such as with iniVation extend event-camera + neuromorphic pipelines for edge AI.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.synsense.ai/">Neuromorphic / event-based products</a></strong> — 2024–2026 — Speck/Xylo-class chips; 2024 iniVation collaboration and funding extend event-based vision + edge AI offerings.</li>
+</ul>
 
 <a name="Sagence"></a>
 <div align="center" class="vendor-logo"><a href="https://www.sagence.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/AnalogInference_logo.png" height="60" loading="lazy" alt="Sagence AI" /></a></div>
 <div align="center" class="vendor-title"><h3>Sagence AI (ex–Analog Inference)</h3></div>
-<p><strong>Analog in-memory compute for datacenter inference (2024–2026)</strong></p>
-<blockquote>
-  <p>Rebranded from Analog Inference (Nov 2024). Continues analog compute-in-memory accelerators aimed at energy-efficient LLM / vision inference; active on the 2025–2026 AI infra circuit.</p>
-</blockquote>
-
-<p>
-
-<p>
-
-<p>
-
-<a name="d-matrix"></a>
-<div align="center" class="vendor-logo"><a href="https://www.d-matrix.ai/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/d-matrix_logo.png" height="60" loading="lazy" alt="d-Matrix" /></a></div>
-<p><strong><a href="https://www.d-matrix.ai/">d-Matrix Corsair — digital in-memory compute for inference</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Corsair accelerators target low-latency / high-efficiency datacenter LLM inference with digital compute-in-memory; expanding software and cloud partner footprint.</p>
-</blockquote>
-
-<a name="Etched"></a>
-<div align="center"><h3><a href="https://www.etched.com/">Etched</a></h3></div>
-
-<p><strong><a href="https://techcrunch.com/2026/06/30/nvidia-competitor-etched-hits-5b-valuation-1b-in-sales-for-ai-chip/">Nvidia competitor Etched hits $5B valuation, $1B in sales for AI chip</a></strong></p>
-<blockquote>
-  <p>Jun 2026 — Etched reports TSMC-manufactured transformer-specialized ASICs powering “frontier inference clusters,” with ~$800M raised to date (including a $500M round at a $5B valuation) and ~$1B in booked system orders.</p>
-</blockquote>
-<p><a href="https://techcrunch.com/2024/06/25/etched-is-building-an-ai-chip-that-only-runs-transformer-models/">Etched is building an AI chip that only runs one type of model</a></p>
-<blockquote>
-  <p>2024 Series A coverage of Sohu: an ASIC purpose-built for Transformer inference (rather than general-purpose GPU programmability), aiming for higher tokens/sec and better energy efficiency on LLM serving.</p>
-</blockquote>
+<p class="vendor-blurb">Sagence AI (rebranded from Analog Inference in Nov 2024) develops analog compute-in-memory accelerators aimed at energy-efficient LLM and vision inference for datacenter and edge deployments—part of the broader analog-AI cohort seeking orders-of-magnitude efficiency vs. digital GPUs on inference.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.sagence.ai/">Analog in-memory compute platform</a></strong> — 2024–2026 — continues analog CIM accelerators after rebrand; active on the AI infra circuit for energy-efficient inference.</li>
+</ul>
 
 <div align="center" class="section-title"><h2><a name="China_AI_Chips"></a>V. China AI Accelerator Vendors</h2></div>
 <hr class="section-rule" />
-
 <p>Domestic training/inference accelerators have become central under export-control and procurement policies. Coverage below highlights widely reported product lines; treat performance claims as vendor-/press-reported unless independently benchmarked.</p>
 
 <a name="HuaweiAscend"></a>
 <div align="center" class="vendor-logo"><a href="https://www.huawei.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HUAWEI_logo.png" height="40" loading="lazy" alt="HUAWEI logo" /></a>
 &nbsp;&nbsp;
 <a href="https://www.hisilicon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HiSilicon_logo.png" height="40" loading="lazy" alt="HiSilicon logo" /></a></div>
-<div align="center"><h3>Huawei Ascend / HiSilicon</h3></div>
-
-<p><strong><a href="https://www.huawei.com/en/news/2025/9/hc-xu-keynote-speech">Huawei Atlas 900 A3 SuperPoD / Ascend 910C</a></strong></p>
-<blockquote>
-  <p>2025 — Huawei scaled Ascend 910B/910C deployments and launched Atlas 900 A3 SuperPoD (up to 384× Ascend 910C), also referred to in cloud form as CloudMatrix384-class supernodes, for large-model training and serving. Ascend 950-class follow-ons are on Huawei’s roadmap.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Huawei Ascend / HiSilicon</h3></div>
+<p class="vendor-blurb">Huawei’s Ascend line (HiSilicon) is China’s most widely deployed domestic training/inference accelerator family. Ascend 910 established the high-end; 910B/910C scaled under export controls into Atlas servers and SuperPoD / CloudMatrix supernodes. Cann/MindSpore software and Atlas systems form a full stack for China large-model train and serve; Ascend 950-class follow-ons are on the roadmap.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.huawei.com/en/news/2025/9/hc-xu-keynote-speech">Atlas 900 A3 SuperPoD / Ascend 910C</a></strong> — 2025 — scaled 910B/910C deployments; Atlas 900 A3 SuperPoD (up to 384× Ascend 910C) / CloudMatrix384-class supernodes; Ascend 950-class on roadmap.</li>
+</ul>
 
 <a name="Cambricon"></a>
 <div align="center" class="vendor-logo"><a href="https://www.cambricon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Cambricon_logo.png" height="50" loading="lazy" alt="Cambricon" /></a></div>
-<div align="center"><h3>Cambricon (寒武纪)</h3></div>
-
-<p><strong><a href="https://www.tomshardware.com/tech-industry/semiconductors/cambricon-targets-500000-ai-chips-in-2026-as-china-accelerates-domestic-hardware-push">Cambricon targets 500,000 AI chips in 2026</a></strong></p>
-<blockquote>
-  <p>Reports describe aggressive 2026 volume plans for Siyuan / MLU-class accelerators (including Siyuan 590 and next-gen 690 messaging), serving Chinese internet/cloud customers as a leading pure-play domestic AI-chip vendor.</p>
-</blockquote>
+<div align="center" class="vendor-title"><h3>Cambricon (寒武纪)</h3></div>
+<p class="vendor-blurb">Cambricon is China’s leading pure-play AI-chip vendor, spanning edge IP/SoCs and cloud MLU / Siyuan accelerators. After an early edge-IP era, the company refocused on cloud training/inference cards for Chinese internet and intelligent-computing customers, with aggressive 2026 volume plans.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.tomshardware.com/tech-industry/semiconductors/cambricon-targets-500000-ai-chips-in-2026-as-china-accelerates-domestic-hardware-push">500,000 AI chips target for 2026</a></strong> — Aggressive 2026 volume plans for Siyuan/MLU-class accelerators (Siyuan 590 and next-gen 690 messaging) for Chinese cloud/internet customers.</li>
+</ul>
 
 <a name="Biren"></a>
-<div align="center"><h3><a href="https://www.birentech.com/">Biren Technology (壁仞)</a></h3></div>
-
-<p><strong><a href="https://www.reuters.com/world/asia-pacific/china-ai-chipmaker-biren-surges-82-hong-kong-debut-kicking-off-2026-listings-2026-01-02/">China AI chipmaker Biren soars in Hong Kong debut</a></strong></p>
-<blockquote>
-  <p>Jan 2026 — Shanghai Biren Technology listed in Hong Kong (6082.HK), raising ~HK$5.58B. Best known for BR100-class GPGPU / AI accelerators positioned as high-performance domestic alternatives.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><a href="https://www.birentech.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Biren_logo.png" height="48" loading="lazy" alt="Biren" /></a></div>
+<div align="center" class="vendor-title"><h3>Biren Technology (壁仞)</h3></div>
+<p class="vendor-blurb">Biren builds high-performance GPGPU / AI accelerators (BR100 class) positioned as domestic alternatives for training and inference. After years of product development under export-control pressure, Biren listed in Hong Kong in early 2026, raising capital to scale production and software.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.reuters.com/world/asia-pacific/china-ai-chipmaker-biren-surges-82-hong-kong-debut-kicking-off-2026-listings-2026-01-02/">Hong Kong listing debut</a></strong> — Jan 2026 — listed 6082.HK, raising ~HK$5.58B; BR100-class accelerators as high-performance domestic alternatives.</li>
+</ul>
 
 <a name="MooreThreads"></a>
-<div align="center"><h3><a href="https://www.mthreads.com/">Moore Threads (摩尔线程)</a></h3></div>
+<div align="center" class="vendor-logo"><a href="https://www.mthreads.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/MooreThreads_logo.png" height="48" loading="lazy" alt="Moore Threads" /></a></div>
+<div align="center" class="vendor-title"><h3>Moore Threads (摩尔线程)</h3></div>
+<p class="vendor-blurb">Moore Threads develops full-function GPUs for graphics and AI (MTT S-series) with the MUSA software stack. The company completed a large STAR Market IPO in late 2025 amid China’s domestic GPU push, targeting intelligent-computing centers and PC/GPU markets.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.cnbc.com/2025/12/05/china-nvidia-moore-threads-trading-debut-1-billion-listing-ipo-shanghai-gpu-enflame-biren.html">Shanghai trading debut</a></strong> — Dec 2025 — ~$1.1B STAR Market IPO; MTT S4000/S5000-class AI cards and MUSA stack in the domestic GPU wave.</li>
+</ul>
 
-<p><strong><a href="https://www.cnbc.com/2025/12/05/china-nvidia-moore-threads-trading-debut-1-billion-listing-ipo-shanghai-gpu-enflame-biren.html">Moore Threads surges over 400% on Shanghai trading debut</a></strong></p>
-<blockquote>
-  <p>Dec 2025 — Full-function GPU vendor (MTT S4000 / S5000 class AI cards, MUSA software stack) completed a ~$1.1B STAR Market IPO amid China’s domestic GPU push.</p>
-</blockquote>
+<a name="MetaX"></a>
+<div align="center" class="vendor-logo"><a href="https://www.metax-tech.com/en/about/about.html"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/MetaX_logo.png" height="48" loading="lazy" alt="MetaX" /></a></div>
+<div align="center" class="vendor-title"><h3>MetaX (沐曦)</h3></div>
+<p class="vendor-blurb">MetaX (沐曦) ships C-series GPUs for training and inference with the MXMACA software stack for AI clusters and intelligent-computing centers. After listing on STAR in Dec 2025, the company reported strong revenue growth into 2026 as domestic GPU demand rose.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.caixinglobal.com/2026-03-26/chinese-gpu-maker-metax-doubles-revenue-amid-push-for-domestic-chips-102427800.html">MetaX doubles revenue</a></strong> — C500 mass production; C600 domestic-process follow-on; STAR 688802.SH (listed Dec 2025) with MXMACA for AI clusters.</li>
+</ul>
 
-<p>
+<a name="Hygon"></a>
+<div align="center" class="vendor-logo"><a href="https://www.hygon.cn/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Hygon_logo.png" height="48" loading="lazy" alt="Hygon" /></a></div>
+<div align="center" class="vendor-title"><h3>Hygon (海光) DCU</h3></div>
+<p class="vendor-blurb">Hygon’s Deep Computing Unit (DCU) accelerators are GPGPU-class chips widely used in China for large-model training and inference, paired with the DTK software stack. Hygon also ships x86 CPUs; the DCU line is the AI-relevant product for intelligent-computing centers.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.trendforce.com/news/2026/04/29/news-huawei-ascend-cambricon-and-hygon-completed-day-0-adaptation-to-deepseek-v4/">DeepSeek-V4 Day-0 adaptation</a></strong> — 2025–2026 — DCU + DTK widely used for LLM train/infer; same-day DeepSeek model adaptations reported with Ascend and Cambricon.</li>
+</ul>
+
+<a name="Iluvatar"></a>
+<div align="center" class="vendor-logo"><a href="https://www.iluvatar.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Iluvatar_logo.png" height="48" loading="lazy" alt="Iluvatar CoreX" /></a></div>
+<div align="center" class="vendor-title"><h3>Iluvatar CoreX (天数智芯)</h3></div>
+<p class="vendor-blurb">Iluvatar CoreX (天数智芯) designs general-purpose GPUs for AI training and inference (BI-V series) with a multi-generation architecture roadmap. Hong Kong-listed, the company publicly frames Tianshu / Tianxuan / Tianji / Tianquan messaging against NVIDIA Hopper→Blackwell→Rubin class platforms.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.scmp.com/tech/big-tech/article/3341368/iluvatar-corex-targets-nvidias-rubin-gpu-road-map-amid-china-chip-push">Roadmap vs. NVIDIA Rubin</a></strong> — Jan 2026 — multi-generation GPU roadmap; BI-V150-class products already in AI train/infer deployments.</li>
+</ul>
 
 <a name="Enflame"></a>
-<div align="center" class="vendor-title"><h3><a href="https://www.enflame-tech.com/">Enflame (燧原)</a></h3></div>
-<p><strong>Enflame CloudBlazer / iGCU accelerators</strong></p>
-<blockquote>
-  <p>2024–2026 — Domestic training/inference GPUs (CloudBlazer series) deployed in China intelligent-computing centers with TopsRider software stack updates for LLM workloads.</p>
-</blockquote>
+<div align="center" class="vendor-logo"><a href="https://www.enflame-tech.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Enflame_logo.png" height="48" loading="lazy" alt="Enflame" /></a></div>
+<div align="center" class="vendor-title"><h3>Enflame (燧原)</h3></div>
+<p class="vendor-blurb">Enflame (燧原) builds CloudBlazer / iGCU accelerators for domestic training and inference in intelligent-computing centers. The TopsRider software stack targets LLM workloads alongside hardware refreshes through 2024–2026.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.enflame-tech.com/">CloudBlazer / iGCU accelerators</a></strong> — 2024–2026 — domestic train/infer GPUs deployed in China ICC sites with TopsRider updates for LLM workloads.</li>
+</ul>
 
 <a name="Horizon"></a>
 <div align="center" class="vendor-logo"><a href="https://www.horizon.auto/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Horizon_Robotics_logo.png" height="50" loading="lazy" alt="Horizon Robotics" /></a></div>
 <div align="center" class="vendor-title"><h3>Horizon Robotics (地平线)</h3></div>
-<p><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Horizon Journey 6 / BPU Nash</a></strong></p>
-<blockquote>
-  <p>2024–2026 — Journey-series automotive AI SoCs (Journey 6 with BPU Nash / stronger Transformer support) widely designed into China passenger vehicles for L2+ assisted driving.</p>
-</blockquote>
+<p class="vendor-blurb">Horizon Robotics (地平线) is a leading China automotive AI SoC vendor. Journey-series chips (through Journey 6 with BPU Nash) power L2+ assisted driving in many passenger vehicles, with increasing Transformer support for BEV/occupancy and end-to-end driving models.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Journey 6 / BPU Nash</a></strong> — 2024–2026 — Journey 6 with stronger Transformer support widely designed into China passenger vehicles for L2+ ADAS.</li>
+</ul>
 
 <a name="BlackSesame"></a>
 <div align="center" class="vendor-logo"><a href="https://www.blacksesame.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Blacksesame_logo.png" height="50" loading="lazy" alt="Black Sesame" /></a></div>
-<div align="center"><h3>Black Sesame Technologies (黑芝麻智能)</h3></div>
-
-<p><strong><a href="https://www.blacksesame.com/en/huashan-a1000/">Huashan A1000 automotive-grade ADAS / AD SoCs</a></strong></p>
-<blockquote>
-  <p>Hong Kong-listed (02533.HK, Aug 2024) automotive AI SoC vendor. Huashan A1000-family chips target L2+/L3 assisted driving with automotive-grade safety certifications and are reported in mass-production vehicle models; Huashan A2000 messaging extends toward higher-performance / cross-domain AI inference.</p>
-</blockquote>
-
-<a name="MetaX"></a>
-<div align="center" class="vendor-title"><h3><a href="https://www.metax-tech.com/en/about/about.html">MetaX (沐曦)</a></h3></div>
-
-<p><strong><a href="https://www.caixinglobal.com/2026-03-26/chinese-gpu-maker-metax-doubles-revenue-amid-push-for-domestic-chips-102427800.html">Chinese GPU Maker MetaX Doubles Revenue Amid Push for Domestic Chips</a></strong></p>
-<blockquote>
-  <p>Shanghai MetaX (STAR: 688802.SH, listed Dec 2025) ships C-series training/inference GPUs (C500 mass production; C600 domestic-process follow-on) with the MXMACA software stack for AI clusters and intelligent-computing centers.</p>
-</blockquote>
-
-<a name="Hygon"></a>
-<div align="center" class="vendor-title"><h3><a href="https://www.hygon.cn/">Hygon (海光) DCU</a></h3></div>
-
-<p><strong><a href="https://www.trendforce.com/news/2026/04/29/news-huawei-ascend-cambricon-and-hygon-completed-day-0-adaptation-to-deepseek-v4/">Hygon DCU completes Day-0 adaptation for DeepSeek-V4</a></strong></p>
-<blockquote>
-  <p>Hygon’s Deep Computing Unit (DCU) GPGPU-class accelerators and DTK software stack are widely used in China for large-model training/inference; 2025–2026 coverage highlights same-day DeepSeek model adaptations alongside Ascend and Cambricon.</p>
-</blockquote>
-
-<a name="Iluvatar"></a>
-<div align="center" class="vendor-title"><h3><a href="https://www.iluvatar.com/">Iluvatar CoreX (天数智芯)</a></h3></div>
+<div align="center" class="vendor-title"><h3>Black Sesame Technologies (黑芝麻智能)</h3></div>
+<p class="vendor-blurb">Black Sesame Technologies (黑芝麻智能) designs automotive-grade ADAS/AD SoCs (Huashan family). Hong Kong-listed in 2024, the company focuses on safety-certified L2+/L3 platforms; A1000-family chips are reported in mass-production vehicles, with A2000 extending toward higher-performance and cross-domain AI.</p>
+<p class="latest-label"><strong>Latest</strong></p>
+<ul class="latest-list">
+<li><strong><a href="https://www.blacksesame.com/en/huashan-a1000/">Huashan A1000 family</a></strong> — HK 02533.HK (Aug 2024); A1000-family for L2+/L3 with automotive safety certifications; A2000 messaging for higher-perf / cross-domain inference.</li>
+</ul>
 
-<p><strong><a href="https://www.scmp.com/tech/big-tech/article/3341368/iluvatar-corex-targets-nvidias-rubin-gpu-road-map-amid-china-chip-push">Iluvatar CoreX targets Nvidia’s Rubin with GPU road map</a></strong></p>
-<blockquote>
-  <p>Jan 2026 — Hong Kong-listed Iluvatar CoreX outlined a multi-generation GPU architecture roadmap (Tianshu / Tianxuan / Tianji / Tianquan messaging) aiming to close the gap with Nvidia Hopper→Blackwell→Rubin class platforms, with BI-V150-class products already in AI training/inference deployments.</p>
-</blockquote>
 
 <a name="AIChipCompilers"></a>
 
@@ -692,3 +689,4 @@
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 </div>
+
diff --git a/assets/css/style.scss b/assets/css/style.scss
index 5290c92..e67926d 100644
--- a/assets/css/style.scss
+++ b/assets/css/style.scss
@@ -125,7 +125,8 @@ a {
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-.updates-list {
+.updates-list,
+.glance-list {
   background: var(--ai-surface);
   border: 1px solid var(--ai-border);
   border-radius: 10px;
@@ -133,11 +134,54 @@ a {
   margin: 0.5rem 0 1.5rem;
 }
 
-.updates-list li {
-  margin: 0.35rem 0;
+.updates-list li,
+.glance-list li {
+  margin: 0.4rem 0;
   line-height: 1.45;
 }
 
+@media (min-width: 820px) {
+  .glance-list {
+    columns: 2;
+    column-gap: 1.75rem;
+  }
+
+  .glance-list li {
+    break-inside: avoid;
+  }
+}
+
+.glance-list a {
+  text-decoration: none;
+}
+
+.glance-list a:hover {
+  text-decoration: underline;
+}
+
+.vendor-blurb {
+  margin: 0.35rem 0 0.85rem;
+  line-height: 1.55;
+  color: var(--ai-ink);
+}
+
+.latest-label {
+  margin: 1rem 0 0.35rem;
+  font-size: 0.95rem;
+  letter-spacing: 0.02em;
+  color: var(--ai-accent);
+}
+
+.latest-list {
+  margin: 0 0 1.35rem;
+  padding-left: 1.2rem;
+}
+
+.latest-list li {
+  margin: 0.4rem 0;
+  line-height: 1.5;
+}
+
 .shortcut-table {
   width: 100%;
   border-collapse: separate;

From d7b1544f2db7578d6ba6dc5af57aff8fc75af7ee Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 04:28:22 +0000
Subject: [PATCH 12/13] =?UTF-8?q?Refresh=20Latest/glance=20entries=20to=20?=
 =?UTF-8?q?2025=E2=80=932026=20news?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Replace stale 2024-centric Latest items with current product and
commercial milestones (Trainium3, Maia 200, Ironwood GA, 8 Elite Gen 5,
Dimensity 9500, Cerebras IPO, Galaxy Blackhole, Corsair production, etc.).

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md | 116 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 63 insertions(+), 53 deletions(-)

diff --git a/README.md b/README.md
index 99859f0..f2a9e57 100644
--- a/README.md
+++ b/README.md
@@ -12,34 +12,34 @@
 <p class="refresh-note" align="center"><em>One-line latest status per company — updated periodically. Last refreshed: July 2026.</em></p>
 <hr class="section-rule" />
 <ul class="glance-list">
-<li><a href="#Nvidia"><strong>NVIDIA</strong></a> — Vera Rubin platform announced for H2 2026; Blackwell NVL72 in volume AI-factory deployments.</li>
-<li><a href="#Intel"><strong>Intel / Habana</strong></a> — Gaudi 3 shipping for enterprise GenAI train/infer with Ethernet scale-out OEM/cloud partners.</li>
+<li><a href="#Nvidia"><strong>NVIDIA</strong></a> — Vera Rubin platform targeted H2 2026; Blackwell NVL72 remains the volume AI-factory workhorse.</li>
+<li><a href="#Intel"><strong>Intel / Habana</strong></a> — Gaudi 3 generally available on IBM Cloud and Dell AI Factory for enterprise GenAI inference/fine-tune.</li>
 <li><a href="#AMD"><strong>AMD</strong></a> — Instinct MI350 shipping; Meta partnership expands multi-GW MI450 / Helios rack deployments.</li>
-<li><a href="#Qualcomm"><strong>Qualcomm</strong></a> — Snapdragon 8 Elite Hexagon NPU leads on-device GenAI; Cloud AI 100 remains the inference SKU.</li>
+<li><a href="#Qualcomm"><strong>Qualcomm</strong></a> — Snapdragon 8 Elite Gen 5 (Sep 2025) Hexagon NPU ~37% faster for on-device agentic AI / LLMs.</li>
 <li><a href="#Samsung"><strong>Samsung</strong></a> — Exynos 2500 (3nm GAA) ships with a substantially upgraded on-device NPU.</li>
-<li><a href="#MediaTek"><strong>MediaTek</strong></a> — Dimensity 9400 NPU 890 targets agentic on-device AI (LoRA, video, MoE LLMs).</li>
-<li><a href="#Marvell"><strong>Marvell</strong></a> — Major custom AI ASIC + DPU partner for hyperscaler accelerators (Trainium/Maia-class programs).</li>
+<li><a href="#MediaTek"><strong>MediaTek</strong></a> — Dimensity 9500 (Sep 2025) NPU 990 + Super Efficient CIM NPU for agentic on-device GenAI.</li>
+<li><a href="#Marvell"><strong>Marvell</strong></a> — Custom AI XPU partner for AWS Trainium / Microsoft Maia; 18+ custom projects and rising AI silicon revenue.</li>
 <li><a href="#Broadcom"><strong>Broadcom</strong></a> — OpenAI Jalapeño / multi-GW XPU deal; extended Meta MTIA partnership; long-time Google TPU partner.</li>
-<li><a href="#Google"><strong>Google</strong></a> — Ironwood (TPU v7) and Trillium (v6e) power Cloud TPU; multi-GW Anthropic capacity deals.</li>
-<li><a href="#Amazon_AWS"><strong>AWS</strong></a> — Trainium2 Trn2 UltraServers generally available; Inferentia2 continues cost-efficient LLM serving.</li>
-<li><a href="#Microsoft"><strong>Microsoft</strong></a> — Azure Maia 100 deployed for Azure AI / Copilot-class workloads; next Maia gens in design.</li>
+<li><a href="#Google"><strong>Google</strong></a> — Ironwood (TPU7x) generally available Mar 2026; Trillium (v6e) remains a volume Cloud TPU SKU.</li>
+<li><a href="#Amazon_AWS"><strong>AWS</strong></a> — Trainium3 / Trn3 UltraServers GA (Dec 2025); Trainium4 previewed with NVLink Fusion path.</li>
+<li><a href="#Microsoft"><strong>Microsoft</strong></a> — Maia 200 inference accelerator (Jan 2026) deploying in Azure for Copilot / Foundry token economics.</li>
 <li><a href="#OpenAI"><strong>OpenAI</strong></a> — First custom inference ASIC “Jalapeño” with Broadcom; initial deployments targeted end of 2026.</li>
 <li><a href="#Anthropic"><strong>Anthropic</strong></a> — Expanded Google TPU + Broadcom capacity (~3.5 GW from 2027) atop AWS Trainium and NVIDIA GPUs.</li>
 <li><a href="#Apple"><strong>Apple</strong></a> — M5 / M5 Pro / M5 Max add GPU Neural Accelerators and a faster Neural Engine for Apple Intelligence.</li>
 <li><a href="#Alibaba"><strong>Alibaba</strong></a> — T-Head PPU / Zhenwu family deployed in Alibaba Cloud and China Unicom-scale clusters.</li>
-<li><a href="#Baidu"><strong>Baidu</strong></a> — Kunlunxin P800-class GPUs remain core to Baidu Cloud / ERNIE train and serve.</li>
-<li><a href="#Tencent_Cloud"><strong>Tencent</strong></a> — Zixiao and partner accelerators continue scaling LLM/recommendation workloads on Tencent Cloud.</li>
+<li><a href="#Baidu"><strong>Baidu</strong></a> — Kunlunxin P800 10k-card clusters; key ERNIE 5.1 training on domestic stack; Tianchi 256-card supernode (2026).</li>
+<li><a href="#Tencent_Cloud"><strong>Tencent</strong></a> — Zixiao / partner accelerators continue scaling LLM and recommendation serving on Tencent Cloud (2025–2026).</li>
 <li><a href="#Facebook"><strong>Meta</strong></a> — Multi-generation MTIA (300–500 class) with Broadcom; modular chiplets through 2026–2027.</li>
 <li><a href="#Tesla"><strong>Tesla</strong></a> — AI5 taped out (Apr 2026); AI6 / Dojo3 in progress; vehicles near-term stay on AI4.</li>
-<li><a href="#ARM"><strong>Arm</strong></a> — Ethos-U85 micro-NPU adds native transformer ops for edge/IoT GenAI with Corstone-320.</li>
-<li><a href="#Synopsys"><strong>Synopsys</strong></a> — ARC NPX NPU IP scales multi-TOPS configurations for auto/consumer/datacenter SoCs.</li>
-<li><a href="#Imagination"><strong>Imagination</strong></a> — PowerVR NNA + Continuum software for edge/automotive transformer-class networks.</li>
-<li><a href="#CEVA"><strong>CEVA</strong></a> — NeuPro-class NPU IP and DNN compiler for embedded, automotive, and IoT inference.</li>
-<li><a href="#Cadence"><strong>Cadence</strong></a> — Tensilica AI Platform / NeuroWeave updates for on-device and automotive transformers.</li>
-<li><a href="#VeriSilicon"><strong>VeriSilicon</strong></a> — VIP/Acuity NPU IP and turnkey design services widely used in China and global edge SoCs.</li>
-<li><a href="#Cerebras"><strong>Cerebras</strong></a> — WSE-3 / CS-3 wafer-scale systems shipping for large-model train and high-throughput inference.</li>
-<li><a href="#Graphcore"><strong>Graphcore</strong></a> — Now a SoftBank subsidiary; continuing next-gen IPU / AI compute under SoftBank backing.</li>
-<li><a href="#Tenstorrent"><strong>Tenstorrent</strong></a> — Wormhole shipping; Blackhole next — Tensix chiplet meshes with open TT-Metalium software.</li>
+<li><a href="#ARM"><strong>Arm</strong></a> — Ethos-U85 + Corstone-320 IoT reference for edge transformer / GenAI; Armv9 SME path in mobile SoCs.</li>
+<li><a href="#Synopsys"><strong>Synopsys</strong></a> — ARC NPX NPU IP scaled for auto/consumer GenAI SoCs with ongoing 2025–2026 toolchain releases.</li>
+<li><a href="#Imagination"><strong>Imagination</strong></a> — PowerVR NNA + Continuum for edge/auto transformer networks; 2025–2026 licensee SoC ramps.</li>
+<li><a href="#CEVA"><strong>CEVA</strong></a> — NeuPro-class NPU IP for embedded/auto/IoT transformers; 2025–2026 DNN compiler updates.</li>
+<li><a href="#Cadence"><strong>Cadence</strong></a> — Tensilica AI Platform / NeuroWeave updates for on-device and automotive transformer SoCs (2025–2026).</li>
+<li><a href="#VeriSilicon"><strong>VeriSilicon</strong></a> — VIP/Acuity NPU IP and turnkey services for China/global edge GenAI SoCs (2025–2026).</li>
+<li><a href="#Cerebras"><strong>Cerebras</strong></a> — Nasdaq IPO (CBRS, May 2026); multi-year OpenAI inference capacity deal (&gt;$20B messaging).</li>
+<li><a href="#Graphcore"><strong>Graphcore</strong></a> — SoftBank injects ~$457M (2026); Izanagi next-gen IPU+CPU path aimed at Stargate-scale 2026 deployments.</li>
+<li><a href="#Tenstorrent"><strong>Tenstorrent</strong></a> — Galaxy Blackhole GA (Apr 2026): 32× Blackhole servers shipping for LLM inference and video gen.</li>
 <li><a href="#Etched"><strong>Etched</strong></a> — ~$5B valuation / ~$1B booked orders for transformer-specialized Sohu inference ASICs.</li>
 <li><a href="#Groq"><strong>Groq</strong></a> — LPU technology licensed into NVIDIA’s Vera Rubin AI-factory stack (GTC 2026).</li>
 <li><a href="#SambaNova"><strong>SambaNova</strong></a> — SN50 RDU for agentic AI; $350M+ Series E; SoftBank early deploy; Intel collab; H2 2026 ship.</li>
@@ -48,10 +48,10 @@
 <li><a href="#Furiosa"><strong>FuriosaAI</strong></a> — RNGD in mass production; Broadcom partnership for next-gen multi-die inference platform.</li>
 <li><a href="#Kinara"><strong>Kinara</strong></a> — NXP acquiring Kinara (ex–Deep Vision) for industrial/automotive edge NPU portfolio.</li>
 <li><a href="#SiMa"><strong>SiMa.ai</strong></a> — Modalix MLSoC in production for Physical AI (robots/industrial) under ~10W with LLiMa LLMs.</li>
-<li><a href="#d-matrix"><strong>d-Matrix</strong></a> — Corsair digital in-memory compute accelerators targeting efficient datacenter LLM inference.</li>
+<li><a href="#d-matrix"><strong>d-Matrix</strong></a> — Corsair inference platform in full production (Jun 2026); $275M Series C / ~$2B valuation.</li>
 <li><a href="#Blaize"><strong>Blaize</strong></a> — Nasdaq-listed (BZAI); GSP architecture for industrial/smart-city edge vision and GenAI.</li>
-<li><a href="#SynSense"><strong>SynSense</strong></a> — Speck/Xylo neuromorphic chips; iniVation collab extends event-based vision + edge AI.</li>
-<li><a href="#Sagence"><strong>Sagence AI</strong></a> — Rebranded from Analog Inference; analog CIM accelerators for efficient LLM/vision inference.</li>
+<li><a href="#SynSense"><strong>SynSense</strong></a> — Speck/Xylo neuromorphic + event-vision stack; 2025–2026 funding/collabs for ultra-low-power edge AI.</li>
+<li><a href="#Sagence"><strong>Sagence AI</strong></a> — Analog CIM accelerators for efficient LLM/vision inference; active 2025–2026 AI-infra engagements post-rebrand.</li>
 <li><a href="#HuaweiAscend"><strong>Huawei Ascend</strong></a> — Ascend 910B/910C + Atlas 900 A3 SuperPoD (CloudMatrix384-class); 950-class on roadmap.</li>
 <li><a href="#Cambricon"><strong>Cambricon</strong></a> — Targets ~500k AI chips in 2026; Siyuan 590 / next-gen 690 messaging for cloud customers.</li>
 <li><a href="#Biren"><strong>Biren</strong></a> — Hong Kong IPO (6082.HK, Jan 2026); BR100-class GPGPU/AI accelerators.</li>
@@ -59,9 +59,9 @@
 <li><a href="#MetaX"><strong>MetaX</strong></a> — STAR-listed (688802.SH); C500 in mass production, C600 domestic-process follow-on, MXMACA stack.</li>
 <li><a href="#Hygon"><strong>Hygon</strong></a> — DCU GPGPUs + DTK stack; Day-0 DeepSeek-V4 adaptation alongside Ascend and Cambricon.</li>
 <li><a href="#Iluvatar"><strong>Iluvatar CoreX</strong></a> — HK-listed; multi-gen GPU roadmap (Tianshu→Tianquan) aiming at Hopper–Rubin class gap; BI-V150 deployed.</li>
-<li><a href="#Enflame"><strong>Enflame</strong></a> — CloudBlazer / iGCU GPUs in China intelligent-computing centers; TopsRider LLM software updates.</li>
-<li><a href="#Horizon"><strong>Horizon Robotics</strong></a> — Journey 6 / BPU Nash designed into China L2+ vehicles with stronger Transformer support.</li>
-<li><a href="#BlackSesame"><strong>Black Sesame</strong></a> — HK-listed; Huashan A1000 in mass-production vehicles; A2000 messaging for higher-perf AD.</li>
+<li><a href="#Enflame"><strong>Enflame</strong></a> — CloudBlazer / iGCU GPUs in China ICC deployments; TopsRider LLM stack updates through 2025–2026.</li>
+<li><a href="#Horizon"><strong>Horizon Robotics</strong></a> — Journey 6 / BPU Nash mass-designed into China L2+ vehicles; stronger Transformer / E2E driving support.</li>
+<li><a href="#BlackSesame"><strong>Black Sesame</strong></a> — Huashan A1000 in mass-production vehicles; A2000 messaging for higher-perf / cross-domain AD (2025–2026).</li>
 </ul>
 
 <div align="center" class="section-title"><h2>Shortcut</h2></div>
@@ -121,8 +121,8 @@
 </ul>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Rubin platform</a></strong> — CES 2026 — Vera Rubin for agentic AI and large MoE; vendor claims large gains in inference token cost and training GPU-efficiency vs. Blackwell; H2 2026 partner availability.</li>
-<li><strong><a href="https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing">Blackwell platform</a></strong> — GTC 2024 — B200 / GB200 Grace Blackwell Superchip and NVL72 systems remain the volume train/serve platform while Rubin ramps.</li>
+<li><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Rubin platform</a></strong> — CES/GTC 2026 — Vera Rubin for agentic AI and large MoE; partner availability targeted H2 2026; Groq-class LPU tech described in the AI-factory stack.</li>
+<li><strong><a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">Blackwell NVL72 in volume</a></strong> — 2025–2026 — B200 / GB200 NVL72 systems are the primary train/serve platform in AI factories while Rubin ramps.</li>
 </ul>
 <p><a href="https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth">Hopper in-depth</a> · <a href="https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/">Hopper</a> · <a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">Blackwell</a></p>
 
@@ -133,7 +133,8 @@
 <p class="vendor-blurb">Intel’s AI accelerator path consolidated around Habana Labs after the 2019 acquisition. Gaudi (training) and Goya (inference) established Ethernet-centric scale-out as an alternative to proprietary GPU fabrics; Gaudi 2 broadened LLM training, and Gaudi 3 (2024) targets enterprise generative AI with OEM and cloud availability. Intel also folds AI into Xeon AMX and Arc/Flex GPUs, but Gaudi remains the dedicated datacenter accelerator brand.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://newsroom.intel.com/artificial-intelligence/vision-2024-enterprise-ai-gaudi-3-open-systems-strategy">Gaudi 3 for enterprise AI</a></strong> — Intel Vision 2024 — Gaudi 3 for GenAI train/infer with Ethernet scale-out; positioned on price/performance vs. GPU alternatives via Dell, HPE, Lenovo, Supermicro and cloud partners.</li>
+<li><strong><a href="https://newsroom.intel.com/artificial-intelligence/ibm-cloud-first-service-provider-deploy-intel-gaudi-3">Gaudi 3 on IBM Cloud</a></strong> — Intel Vision 2025 — IBM Cloud is the first CSP with Gaudi 3 in production (Frankfurt, Washington D.C., Dallas) for enterprise GenAI price/performance.</li>
+<li><strong><a href="https://newsroom.intel.com/artificial-intelligence/intel-gaudi-3-expands-availability-drive-ai-innovation-scale">Dell AI Factory with Gaudi 3</a></strong> — 2025 — Dell AI platform / AI Factory ships validated Gaudi 3 systems for scalable enterprise GenAI with open Ethernet networking.</li>
 </ul>
 
 <a name="AMD"></a>
@@ -158,8 +159,8 @@
 <p class="vendor-blurb">Qualcomm’s AI silicon spans mobile Hexagon NPUs inside Snapdragon SoCs and Cloud AI inference accelerators for power-efficient serving. Hexagon evolved from DSP blocks into fused scalar/vector/tensor NPUs for on-device multimodal GenAI; Cloud AI 100 targets edge/cloud inference outside GPU clusters. Recent flagships (8 Gen 3 → 8 Elite) emphasize agentic and multimodal experiences at the handset.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.androidauthority.com/snapdragon-8-elite-3491805/">Snapdragon 8 Elite — Hexagon NPU</a></strong> — Snapdragon Summit 2024 — Oryon CPU + redesigned Hexagon NPU; Qualcomm cites ~45% faster NPU and better perf/W vs. 8 Gen 3 for on-device GenAI.</li>
-<li><strong><a href="https://www.qualcomm.com/products/technology/processors/cloud-artificial-intelligence/cloud-ai-100">Cloud AI 100</a></strong> — Cloud/edge inference accelerator focused on power-efficient deployment outside traditional GPU clusters.</li>
+<li><strong><a href="https://www.androidauthority.com/qualcomm-snapdragon-8-elite-gen-5-3599341/">Snapdragon 8 Elite Gen 5</a></strong> — Snapdragon Summit 2025 — next Hexagon NPU (~37% faster, ~16% better perf/W vs. 8 Elite) for on-device LLMs and agentic AI; Xiaomi 17 among first devices.</li>
+<li><strong><a href="https://markvena.medium.com/snapdragon-summit-2025-qualcomms-confident-leap-into-the-ai-future-989e0acc319a">Snapdragon X2 Elite NPU</a></strong> — Sep 2025 — X2 Elite / X2 Elite Extreme PC platforms with Hexagon NPU at ~80 TOPS for Copilot+ / on-device AI laptops.</li>
 </ul>
 
 <a name="Samsung"></a>
@@ -177,7 +178,7 @@
 <p class="vendor-blurb">MediaTek’s Dimensity flagship line pairs Arm CPUs with successive in-house NPUs for Android on-device AI. The NPU roadmap (through 8th-gen NPU 890 on Dimensity 9400) emphasizes generative and agentic workloads—LoRA adaptation, diffusion video, and MoE LLMs—alongside the Dimensity Agentic AI Engine software path.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.mediatek.com/press-room/mediateks-dimensity-9400-flagship-soc-offers-extreme-performance-and-efficiency-for-the-latest-ai-experiences">Dimensity 9400 — NPU 890</a></strong> — Oct 2024 — TSMC 3nm flagship with 8th-gen NPU; on-device LoRA, video generation, MoE LLMs, and Dimensity Agentic AI Engine vs. Dimensity 9300.</li>
+<li><strong><a href="https://www.mediatek.com/press-room/mediatek-dimensity-9500-unleashes-best-in-class-performance-ai-experiences-and-power-efficiency-for-the-next-generation-of-mobile-devices">Dimensity 9500 — NPU 990</a></strong> — Sep 2025 — 9th-gen NPU 990 with Generative AI Engine 2.0 (vendor: ~2× compute, 4K image gen, 128K-token LLM path) plus Super Efficient CIM NPU for always-on AI.</li>
 </ul>
 
 <a name="Marvell"></a>
@@ -186,7 +187,8 @@
 <p class="vendor-blurb">Marvell supplies DPUs (OCTEON / ARMADA) and, increasingly, co-designed custom AI accelerators for cloud providers. Alongside Broadcom’s XPU franchise, Marvell is widely reported as a design partner on AWS Trainium/Inferentia-class and Microsoft Maia-class silicon—making it a key “picks and shovels” vendor in the custom-ASIC wave.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.marvell.com/products/data-processing-units.html">Custom AI ASICs + DPUs</a></strong> — 2024–2026 — custom AI accelerator programs for hyperscalers complement OCTEON/ARMADA DPUs and Ethernet/optical connectivity.</li>
+<li><strong><a href="https://www.marvell.com/blogs/inside-marvell-expanding-custom-silicon-opportunity.html">Expanding custom silicon opportunity</a></strong> — Jul 2025 — Custom AI Investor Event: ~18 active custom projects (hyperscaler XPUs + attach) with TAM for custom XPUs growing into the tens of billions by 2028.</li>
+<li><strong><a href="https://www.marvell.com/products/data-processing-units.html">Hyperscaler AI ASIC programs</a></strong> — 2025–2026 — widely reported design partner on AWS Trainium-class and Microsoft Maia-class accelerators, plus AI optics/DPU attach silicon.</li>
 </ul>
 
 <a name="Broadcom"></a>
@@ -229,8 +231,8 @@
 </ul>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://cloud.google.com/blog/products/compute/introducing-trillium-6th-gen-tpus">Trillium (TPU v6e)</a></strong> — Sixth-generation Cloud TPU for foundation-model training and serving; large per-chip compute/efficiency gains vs. v5e.</li>
-<li><strong><a href="https://docs.cloud.google.com/tpu/docs/release-notes">Ironwood (TPU v7) notes</a></strong> — Seventh-generation Cloud TPU family for large-scale training and inference (LLMs, MoEs, diffusion).</li>
+<li><strong><a href="https://docs.cloud.google.com/tpu/docs/release-notes">Ironwood (TPU7x) GA</a></strong> — Mar 31, 2026 — TPU7x (Ironwood family) generally available for large-scale training and inference (LLMs, MoEs, diffusion); up to 9,216-chip pods.</li>
+<li><strong><a href="https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/ironwood-google-tpu-things-to-know/">Ironwood for the age of inference</a></strong> — 2025–2026 — 7th-gen TPU purpose-built for high-volume low-latency inference/serving; Google cites large per-chip gains vs. prior gen.</li>
 </ul>
 <p><a href="https://cloud.google.com/transform/ai-specialized-chips-tpu-history-gen-ai">10 years of TPU</a> · <a href="https://docs.cloud.google.com/tpu/docs/v6e">v6e docs</a> · <a href="https://cloud.google.com/tpu">Cloud TPU</a> · <a href="https://cloud.google.com/edge-tpu/">Edge TPU</a></p>
 
@@ -240,7 +242,8 @@
 <p class="vendor-blurb">AWS builds first-party AI silicon for EC2: Inferentia for inference and Trainium for training, compiled via the Neuron SDK. Inferentia1/2 lowered LLM serving cost on Inf1/Inf2; Trainium1 and Trainium2 (Trn1/Trn2) plus UltraServers with NeuronLink scale-up target foundation-model training and large inference inside AWS regions—often co-designed with partners such as Marvell/Annapurna.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/">EC2 Trn2 / Trainium2 UltraServers</a></strong> — 2024–2025 — Trainium2 Trn2 instances and UltraServers for GenAI train/infer with NeuronLink; Inf2 remains the cost-efficient serving path.</li>
+<li><strong><a href="https://aws.amazon.com/about-aws/whats-new/2025/12/amazon-ec2-trn3-ultraservers/">EC2 Trn3 UltraServers (Trainium3)</a></strong> — Dec 2025 re:Invent — Trainium3 (first 3nm AWS AI chip) UltraServers GA; AWS cites up to ~4.4× compute and ~4× energy efficiency vs. Trn2 UltraServers.</li>
+<li><strong><a href="https://press.aboutamazon.com/2025/12/trainium3-ultraservers-now-available-enabling-customers-to-train-and-deploy-ai-models-faster-at-lower-cost">Trainium3 UltraServers details</a></strong> — Up to 144 Trainium3 chips / ~362 FP8 PFLOPs per UltraServer; Anthropic and Bedrock among early Trainium users; Trainium4 on the roadmap.</li>
 </ul>
 
 <a name="Microsoft"></a>
@@ -249,7 +252,8 @@
 <p class="vendor-blurb">Microsoft entered custom AI silicon with Azure Maia (accelerator) and Cobalt (Arm CPU) to optimize Azure OpenAI / Copilot economics. Maia 100 (5nm, advanced packaging) is co-designed for Azure AI cloud workloads with a PyTorch/ONNX Runtime software path; subsequent Maia generations are expected to deepen rack-level integration with Azure networking and cooling.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://azure.microsoft.com/en-us/blog/azure-maia-for-the-era-of-ai-from-silicon-to-software-to-systems/">Azure Maia for the era of AI</a></strong> — 2024 — first in-house AI accelerator Maia 100 for Azure AI / Copilot-class workloads, with PyTorch and ONNX Runtime integration.</li>
+<li><strong><a href="https://blogs.microsoft.com/blog/2026/01/26/maia-200-the-ai-accelerator-built-for-inference/">Maia 200 inference accelerator</a></strong> — Jan 2026 — TSMC 3nm inference ASIC with 216GB HBM3e; Microsoft cites ~30% better perf/$ vs. prior fleet hardware and FP4/FP8 leadership claims vs. other custom silicon.</li>
+<li><strong><a href="https://news.microsoft.com/source/emea/2026/01/microsoft-introduces-maia-200-new-inference-accelerator-enhances-ai-performance-in-azure/">Maia 200 Azure rollout</a></strong> — Deploying in US Central (then US West 3) for Superintelligence / Foundry / Copilot workloads; Maia SDK preview with PyTorch and Triton.</li>
 </ul>
 
 <a name="OpenAI"></a>
@@ -295,7 +299,8 @@
 <p class="vendor-blurb">Baidu’s Kunlunxin (昆仑芯) GPUs underpin ERNIE and Baidu Cloud AI services. From earlier Kunlun generations through P800-class training/inference cards, Baidu has iterated a full stack (hardware + software) as a China-domestic alternative for large-model workloads.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.baidu.com/">Kunlunxin P800-class</a></strong> — 2024–2026 — P800-class and follow-ons remain a core China train/infer option in Baidu Cloud / ERNIE deployments with ongoing software updates.</li>
+<li><strong><a href="https://www.jiemian.com/article/14421027.html">Kunlunxin trains key ERNIE 5.1 version</a></strong> — Baidu Create 2026 — P800 completed large-scale validation with multiple 10k-card clusters since 2025; fully domestic Kunlunxin cluster trained a key ERNIE 5.1 version (97% effective training rate claimed).</li>
+<li><strong><a href="https://www.jiemian.com/article/14421027.html">Tianchi 256-card supernode</a></strong> — May 2026 — Tianchi 256-card Kunlunxin supernode lit up, June launch messaging; +25% throughput / +50% inference efficiency claims vs. prior gen, HPN5.0 networking.</li>
 </ul>
 
 <a name="Tencent_Cloud"></a>
@@ -304,7 +309,7 @@
 <p class="vendor-blurb">Tencent deploys proprietary Zixiao-class and partner AI accelerators across Tencent Cloud for recommendation, multimodal, and LLM train/infer inside its China footprint—part of a broader mix that also includes merchant GPUs where available.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong>Tencent Cloud AI accelerators</strong> — 2024–2026 — continued proprietary and partner accelerator deployments for recommendation, multimodal, and LLM workloads on Tencent Cloud.</li>
+<li><strong>Tencent Cloud AI accelerators</strong> — 2025–2026 — proprietary Zixiao-class and partner AI accelerators deployed for recommendation, multimodal, and LLM train/infer inside Tencent Cloud.</li>
 </ul>
 
 <a name="Facebook"></a>
@@ -333,7 +338,8 @@
 <p class="vendor-blurb">Arm licenses Ethos NPU IP alongside Cortex CPUs for edge and IoT SoCs. The Ethos-U micro-NPU line (U55 → U65 → U85) scales MAC arrays and adds transformer operators while keeping the Vela/Ethos toolchain path; larger Ethos-N products address higher-TOPS vision/GenAI in Cortex-A systems.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85">Ethos-U85</a></strong> — Third-gen Ethos-U (128–2048 MACs, up to ~4 TOPS @ 1 GHz) with native transformer support; pairs with Cortex-M/A and Corstone-320.</li>
+<li><strong><a href="https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85">Ethos-U85 micro-NPU</a></strong> — Third-gen Ethos-U (to ~4 TOPS @ 1 GHz) with native transformer ops for edge/IoT GenAI; Corstone-320 reference and Vela toolchain.</li>
+<li><strong><a href="https://www.techinsights.com/blog/dimensity-9500-debuts-arm-c1-cores-and-dual-npu">Arm C1 / SME2 in flagship SoCs</a></strong> — 2025 — Arm Lumex CSS (C1 cores + SME2) ships in MediaTek Dimensity 9500-class SoCs, accelerating on-CPU ML alongside NPUs.</li>
 </ul>
 
 <a name="Synopsys"></a>
@@ -342,7 +348,7 @@
 <p class="vendor-blurb">Synopsys DesignWare ARC NPX provides licensable neural processor IP for SoC integrators across automotive, consumer, and infrastructure. The family emphasizes scalable TOPS, functional-safety options, and toolchain continuity for DNN/transformer graphs.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.synopsys.com/designware-ip/processor-solutions/neural-processing-unit.html">ARC NPX NPU IP</a></strong> — 2024–2026 — scalable NPX configurations with ongoing toolchain updates for modern DNN / transformer workloads.</li>
+<li><strong><a href="https://www.synopsys.com/designware-ip/processor-solutions/neural-processing-unit.html">ARC NPX NPU IP</a></strong> — 2025–2026 — DesignWare ARC NPX neural processor IP for automotive/consumer/infrastructure SoCs; multi-TOPS configs and transformer-ready toolchain updates.</li>
 </ul>
 
 <a name="Imagination"></a>
@@ -351,7 +357,7 @@
 <p class="vendor-blurb">Imagination’s PowerVR Neural Network Accelerators (NNA) and GPU+NNA combinations target edge and automotive SoCs. IMG Continuum software maps CNN and transformer-class networks onto the NNA datapaths for licensees.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.imaginationtech.com/products/ai/">Imagination AI / NNA IP</a></strong> — 2024–2026 — PowerVR NNA and GPU+NNA IP with Continuum for edge and automotive transformer-class networks.</li>
+<li><strong><a href="https://www.imaginationtech.com/products/ai/">Imagination AI / NNA IP</a></strong> — 2025–2026 — PowerVR Neural Network Accelerators and GPU+NNA combinations for edge and automotive SoCs with Continuum software for transformer-class nets.</li>
 </ul>
 
 <a name="CEVA"></a>
@@ -360,7 +366,7 @@
 <p class="vendor-blurb">CEVA licenses low-power AI processor IP and DSP/NPU hybrids for real-time embedded, automotive, and IoT inference. NeuPro-class offerings and the CEVA DNN compiler add transformer-capable paths for on-device GenAI and vision.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.ceva-dsp.com/app/deep-learning/">CEVA edge AI / NPU IP</a></strong> — 2024–2026 — low-power AI IP and graph compiler including transformer-capable NeuPro-class products.</li>
+<li><strong><a href="https://www.ceva-dsp.com/app/deep-learning/">CEVA edge AI / NeuPro IP</a></strong> — 2025–2026 — low-power NeuPro-class AI processor IP and DNN graph compiler for real-time embedded, automotive, and IoT inference including transformers.</li>
 </ul>
 
 <a name="Cadence"></a>
@@ -369,7 +375,7 @@
 <p class="vendor-blurb">Cadence Tensilica offers configurable NPU and AI DSP IP for on-device and automotive SoCs. The NeuroWeave toolchain maps modern DNN and transformer graphs onto Tensilica datapaths used widely in vision, audio, and edge GenAI chips.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Tensilica AI Platform</a></strong> — 2024–2026 — Tensilica NPU/AI DSP IP with NeuroWeave updates for DNN / transformer workloads.</li>
+<li><strong><a href="https://www.cadence.com/en_US/home/tools/ip/tensilica-ip/tensilica-ai-platform.html">Tensilica AI Platform</a></strong> — 2025–2026 — Tensilica NPU/AI DSP IP with NeuroWeave toolchain updates for modern DNN and transformer workloads in edge/auto SoCs.</li>
 </ul>
 
 <a name="VeriSilicon"></a>
@@ -378,7 +384,7 @@
 <p class="vendor-blurb">VeriSilicon provides VIP/Acuity NPU IP plus turnkey ASIC design services, especially strong in China edge and vision SoCs. The IP covers CNN through on-device GenAI use cases for consumer, automotive, and IoT customers.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.verisilicon.com/">VIP / NPU IP &amp; design services</a></strong> — 2024–2026 — Acuity/VIP NPU IP and turnkey services for vision and on-device GenAI SoCs.</li>
+<li><strong><a href="https://www.verisilicon.com/">VIP / NPU IP & design services</a></strong> — 2025–2026 — Acuity/VIP NPU IP plus turnkey ASIC design services widely used in China and global edge SoCs for vision and on-device GenAI.</li>
 </ul>
 
 <div align="center" class="section-title"><h2><a name="Startups"></a>IV. Startups</h2></div>
@@ -389,7 +395,8 @@
 <p class="vendor-blurb">Cerebras builds wafer-scale AI processors (WSE) that place an enormous on-wafer SRAM/compute fabric into CS systems, avoiding many multi-GPU communication bottlenecks. WSE-1/2 established the approach; WSE-3 (2024, 5nm) powers CS-3 for foundation-model training and high-throughput inference with cloud and on-prem deployments.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.cerebras.net/press-release/cerebras-announces-third-generation-wafer-scale-engine">WSE-3 announcement</a></strong> — Mar 2024 — WSE-3 (~4T transistors, ~900k AI cores, 125 petaflops peak AI) doubles WSE-2 performance at similar power/price for CS-3.</li>
+<li><strong><a href="https://www.cnbc.com/2026/05/16/cerebras-blockbuster-ipo-boosts-hype-for-spacex-openai-anthropic.html">Cerebras IPO (CBRS)</a></strong> — May 2026 — blockbuster Nasdaq debut after filing; WSE-3 / CS-3 wafer-scale systems power Training and Inference Cloud services.</li>
+<li><strong><a href="https://www.cnbc.com/2026/04/17/cerebras-new-ipo-ai-chips.html">OpenAI multi-year capacity deal</a></strong> — 2025–2026 — OpenAI commits to large inference capacity (filings cite 750MW committed, option to 2GW) plus $1B infrastructure loan / warrants.</li>
 </ul>
 
 <a name="Graphcore"></a>
@@ -398,7 +405,8 @@
 <p class="vendor-blurb">Graphcore pioneered Intelligence Processing Units (IPU) with fine-grained MIMD parallelism and large on-chip memory (Colossus Mk1/Mk2, Bow). After competing in the pre-LLM and early-LLM era, SoftBank acquired Graphcore (2024) as a wholly owned subsidiary to fund next-generation AI compute while keeping the Graphcore brand.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.graphcore.ai/posts/graphcore-joins-softbank-group-to-build-next-generation-of-ai-compute">Graphcore joins SoftBank Group</a></strong> — Jul 2024 — SoftBank acquisition (terms undisclosed; press estimates ~$500M); Graphcore continues under its brand with SoftBank-backed next-gen development.</li>
+<li><strong><a href="https://www.cnbc.com/2026/05/12/softbank-graphcore-ai-chip-investment.html">SoftBank $457M funding into Graphcore</a></strong> — May 2026 — SoftBank funnels ~$457M into Graphcore (wholly owned since Jul 2024) to fund next-gen AI silicon and SoftBank AI infra plans.</li>
+<li><strong><a href="https://www.jonpeddie.com/news/graphcores-ipu-doing-well-at-softbank/">Izanagi / SoftBank AI stack</a></strong> — 2025–2026 — next-gen accelerator work (Izanagi messaging) pairs Graphcore IPU heritage with SoftBank/Ampere Arm CPU stack for hyperscale deployment targets.</li>
 </ul>
 
 <a name="Tenstorrent"></a>
@@ -407,7 +415,8 @@
 <p class="vendor-blurb">Tenstorrent (Jim Keller–led) builds Tensix-based AI processors as scalable chiplet meshes with an open software stack (TT-Metalium). Wormhole-generation cards are shipping into developer and rack deployments; Blackhole is the next architecture step toward higher-density train/infer systems.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://tenstorrent.com/">Wormhole / Blackhole</a></strong> — 2024–2026 — Wormhole shipping; Blackhole next; open TT-Metalium and growing rack-scale deployments on chiplet meshes.</li>
+<li><strong><a href="https://tenstorrent.com/en/newsroom/tenstorrent-enables-ai-at-scale-with-industry-leading-performance">Galaxy Blackhole general availability</a></strong> — Apr 28, 2026 — 6U air-cooled Galaxy with 32 Blackhole chips shipping (vendor: 23 PFLOPS Block FP8, Ethernet scale-out); from ~$110k per server.</li>
+<li><strong><a href="https://tenstorrent.com/en/newsroom/tt-deploy">TT-Deploy production shipping</a></strong> — May 2026 — TT-Deploy: volume shipping, multi-Galaxy superclusters, open TT-Metalium stack, and third-party validated serving benchmarks.</li>
 </ul>
 
 <a name="Etched"></a>
@@ -490,7 +499,8 @@
 <p class="vendor-blurb">d-Matrix builds digital compute-in-memory accelerators (Corsair) for low-latency, high-efficiency datacenter LLM inference—contrasting analog CIM approaches with a digital datapath while still attacking the memory-wall bottleneck that dominates decode.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.d-matrix.ai/">Corsair inference accelerators</a></strong> — 2024–2026 — Corsair for datacenter LLM inference with expanding software and cloud-partner footprint.</li>
+<li><strong><a href="https://www.d-matrix.ai/announcements/d-matrix-corsair-ai-inference-platform-enters-full-production-to-meet-customer-demand/">Corsair enters full production</a></strong> — Jun 2026 — Corsair digital in-memory inference platform in volume production (TSMC N6); shipping to priority hyperscalers, neoclouds, and frontier labs.</li>
+<li><strong><a href="https://cryptobriefing.com/d-matrix-corsair-challenges-nvidia-ai-inference/">Series C / $2B valuation</a></strong> — Nov 2025 — $275M Series C (incl. Microsoft M12) at ~$2B valuation to scale Corsair rack-scale inference.</li>
 </ul>
 
 <a name="Blaize"></a>
@@ -508,7 +518,7 @@
 <p class="vendor-blurb">SynSense (formerly aiCTX) builds neuromorphic and event-based sensing+compute chips (Speck, Xylo) for ultra-low-power always-on perception. Collaborations such as with iniVation extend event-camera + neuromorphic pipelines for edge AI.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.synsense.ai/">Neuromorphic / event-based products</a></strong> — 2024–2026 — Speck/Xylo-class chips; 2024 iniVation collaboration and funding extend event-based vision + edge AI offerings.</li>
+<li><strong><a href="https://www.synsense.ai/">SynSense neuromorphic products</a></strong> — 2025–2026 — Speck/Xylo-class neuromorphic chips and event-based vision+compute offerings (incl. prior iniVation collaboration) for always-on edge sensing.</li>
 </ul>
 
 <a name="Sagence"></a>
@@ -517,7 +527,7 @@
 <p class="vendor-blurb">Sagence AI (rebranded from Analog Inference in Nov 2024) develops analog compute-in-memory accelerators aimed at energy-efficient LLM and vision inference for datacenter and edge deployments—part of the broader analog-AI cohort seeking orders-of-magnitude efficiency vs. digital GPUs on inference.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.sagence.ai/">Analog in-memory compute platform</a></strong> — 2024–2026 — continues analog CIM accelerators after rebrand; active on the AI infra circuit for energy-efficient inference.</li>
+<li><strong><a href="https://www.sagence.ai/">Sagence AI analog CIM</a></strong> — 2025–2026 — continues analog compute-in-memory accelerators (ex–Analog Inference) aimed at energy-efficient datacenter/edge LLM and vision inference.</li>
 </ul>
 
 <div align="center" class="section-title"><h2><a name="China_AI_Chips"></a>V. China AI Accelerator Vendors</h2></div>
@@ -595,7 +605,7 @@
 <p class="vendor-blurb">Enflame (燧原) builds CloudBlazer / iGCU accelerators for domestic training and inference in intelligent-computing centers. The TopsRider software stack targets LLM workloads alongside hardware refreshes through 2024–2026.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.enflame-tech.com/">CloudBlazer / iGCU accelerators</a></strong> — 2024–2026 — domestic train/infer GPUs deployed in China ICC sites with TopsRider updates for LLM workloads.</li>
+<li><strong><a href="https://www.enflame-tech.com/">CloudBlazer / iGCU accelerators</a></strong> — 2025–2026 — domestic train/infer GPUs deployed in China intelligent-computing centers with TopsRider software updates for LLM workloads.</li>
 </ul>
 
 <a name="Horizon"></a>
@@ -604,7 +614,7 @@
 <p class="vendor-blurb">Horizon Robotics (地平线) is a leading China automotive AI SoC vendor. Journey-series chips (through Journey 6 with BPU Nash) power L2+ assisted driving in many passenger vehicles, with increasing Transformer support for BEV/occupancy and end-to-end driving models.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Journey 6 / BPU Nash</a></strong> — 2024–2026 — Journey 6 with stronger Transformer support widely designed into China passenger vehicles for L2+ ADAS.</li>
+<li><strong><a href="https://www.horizon.auto/en/solutions/horizon-journey/horizon-journey6">Journey 6 / BPU Nash</a></strong> — 2025–2026 — Journey 6 automotive AI SoCs with BPU Nash and stronger Transformer support widely designed into China passenger vehicles for L2+ ADAS.</li>
 </ul>
 
 <a name="BlackSesame"></a>
@@ -613,7 +623,7 @@
 <p class="vendor-blurb">Black Sesame Technologies (黑芝麻智能) designs automotive-grade ADAS/AD SoCs (Huashan family). Hong Kong-listed in 2024, the company focuses on safety-certified L2+/L3 platforms; A1000-family chips are reported in mass-production vehicles, with A2000 extending toward higher-performance and cross-domain AI.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.blacksesame.com/en/huashan-a1000/">Huashan A1000 family</a></strong> — HK 02533.HK (Aug 2024); A1000-family for L2+/L3 with automotive safety certifications; A2000 messaging for higher-perf / cross-domain inference.</li>
+<li><strong><a href="https://www.blacksesame.com/en/huashan-a1000/">Huashan A1000 / A2000 path</a></strong> — 2025–2026 — A1000-family chips reported in mass-production L2+/L3 vehicles; A2000 messaging extends toward higher-performance and cross-domain AI inference.</li>
 </ul>
 
 

From ccae2133742afbf8d85f942ece4a508298442486 Mon Sep 17 00:00:00 2001
From: Cursor Agent <cursoragent@cursor.com>
Date: Thu, 16 Jul 2026 04:36:59 +0000
Subject: [PATCH 13/13] Add Qualcomm Dragonfly data-center AI and refresh other
 Latest items

Cover Dragonfly AI200/AI250/AI300 + HBC and Meta C1000 deal; also update
NVIDIA Vera Rubin production, Samsung Exynos 2600, Huawei Atlas 950,
Meta MTIA Iris, Alibaba Zhenwu M890, Cambricon Siyuan 690, and AMD Helios.

Co-authored-by: Shan Tang <shan.tang.g@gmail.com>
---
 README.md | 59 ++++++++++++++++++++++++++++++-------------------------
 1 file changed, 32 insertions(+), 27 deletions(-)

diff --git a/README.md b/README.md
index f2a9e57..227a5a0 100644
--- a/README.md
+++ b/README.md
@@ -9,27 +9,27 @@
 </header>
 
 <div align="center" class="section-title"><h2>At a glance</h2></div>
-<p class="refresh-note" align="center"><em>One-line latest status per company — updated periodically. Last refreshed: July 2026.</em></p>
+<p class="refresh-note" align="center"><em>One-line latest status per company — updated periodically. Last refreshed: July 2026 (incl. Qualcomm Dragonfly data-center AI).</em></p>
 <hr class="section-rule" />
 <ul class="glance-list">
-<li><a href="#Nvidia"><strong>NVIDIA</strong></a> — Vera Rubin platform targeted H2 2026; Blackwell NVL72 remains the volume AI-factory workhorse.</li>
+<li><a href="#Nvidia"><strong>NVIDIA</strong></a> — Vera Rubin platform in full production (GTC 2026); NVL72 partner shipments targeted H2 2026.</li>
 <li><a href="#Intel"><strong>Intel / Habana</strong></a> — Gaudi 3 generally available on IBM Cloud and Dell AI Factory for enterprise GenAI inference/fine-tune.</li>
-<li><a href="#AMD"><strong>AMD</strong></a> — Instinct MI350 shipping; Meta partnership expands multi-GW MI450 / Helios rack deployments.</li>
-<li><a href="#Qualcomm"><strong>Qualcomm</strong></a> — Snapdragon 8 Elite Gen 5 (Sep 2025) Hexagon NPU ~37% faster for on-device agentic AI / LLMs.</li>
-<li><a href="#Samsung"><strong>Samsung</strong></a> — Exynos 2500 (3nm GAA) ships with a substantially upgraded on-device NPU.</li>
+<li><a href="#AMD"><strong>AMD</strong></a> — Helios / MI455X rack-scale on track for H2 2026; Meta 6GW Instinct deal incl. custom MI450-class.</li>
+<li><a href="#Qualcomm"><strong>Qualcomm</strong></a> — Dragonfly data-center AI: AI200/AI250 (2026–27) + AI300/HBC; Meta multi-gen deal for Dragonfly C1000 CPU.</li>
+<li><a href="#Samsung"><strong>Samsung</strong></a> — Exynos 2600 (first 2nm GAA mobile SoC) ships in Galaxy S26 with large on-device GenAI NPU gains.</li>
 <li><a href="#MediaTek"><strong>MediaTek</strong></a> — Dimensity 9500 (Sep 2025) NPU 990 + Super Efficient CIM NPU for agentic on-device GenAI.</li>
 <li><a href="#Marvell"><strong>Marvell</strong></a> — Custom AI XPU partner for AWS Trainium / Microsoft Maia; 18+ custom projects and rising AI silicon revenue.</li>
-<li><a href="#Broadcom"><strong>Broadcom</strong></a> — OpenAI Jalapeño / multi-GW XPU deal; extended Meta MTIA partnership; long-time Google TPU partner.</li>
+<li><a href="#Broadcom"><strong>Broadcom</strong></a> — OpenAI Jalapeño Intelligence Processor unveiled; continues Google TPU + Meta MTIA XPU franchise.</li>
 <li><a href="#Google"><strong>Google</strong></a> — Ironwood (TPU7x) generally available Mar 2026; Trillium (v6e) remains a volume Cloud TPU SKU.</li>
 <li><a href="#Amazon_AWS"><strong>AWS</strong></a> — Trainium3 / Trn3 UltraServers GA (Dec 2025); Trainium4 previewed with NVLink Fusion path.</li>
 <li><a href="#Microsoft"><strong>Microsoft</strong></a> — Maia 200 inference accelerator (Jan 2026) deploying in Azure for Copilot / Foundry token economics.</li>
 <li><a href="#OpenAI"><strong>OpenAI</strong></a> — First custom inference ASIC “Jalapeño” with Broadcom; initial deployments targeted end of 2026.</li>
 <li><a href="#Anthropic"><strong>Anthropic</strong></a> — Expanded Google TPU + Broadcom capacity (~3.5 GW from 2027) atop AWS Trainium and NVIDIA GPUs.</li>
 <li><a href="#Apple"><strong>Apple</strong></a> — M5 / M5 Pro / M5 Max add GPU Neural Accelerators and a faster Neural Engine for Apple Intelligence.</li>
-<li><a href="#Alibaba"><strong>Alibaba</strong></a> — T-Head PPU / Zhenwu family deployed in Alibaba Cloud and China Unicom-scale clusters.</li>
+<li><a href="#Alibaba"><strong>Alibaba</strong></a> — Zhenwu M890 (144GB HBM3) in mass production; 560k+ Zhenwu shipments; roadmap to V900/J900.</li>
 <li><a href="#Baidu"><strong>Baidu</strong></a> — Kunlunxin P800 10k-card clusters; key ERNIE 5.1 training on domestic stack; Tianchi 256-card supernode (2026).</li>
 <li><a href="#Tencent_Cloud"><strong>Tencent</strong></a> — Zixiao / partner accelerators continue scaling LLM and recommendation serving on Tencent Cloud (2025–2026).</li>
-<li><a href="#Facebook"><strong>Meta</strong></a> — Multi-generation MTIA (300–500 class) with Broadcom; modular chiplets through 2026–2027.</li>
+<li><a href="#Facebook"><strong>Meta</strong></a> — MTIA “Iris” (v3) enters production Sep 2026 with Broadcom/TSMC; six-month chip cadence through 2027.</li>
 <li><a href="#Tesla"><strong>Tesla</strong></a> — AI5 taped out (Apr 2026); AI6 / Dojo3 in progress; vehicles near-term stay on AI4.</li>
 <li><a href="#ARM"><strong>Arm</strong></a> — Ethos-U85 + Corstone-320 IoT reference for edge transformer / GenAI; Armv9 SME path in mobile SoCs.</li>
 <li><a href="#Synopsys"><strong>Synopsys</strong></a> — ARC NPX NPU IP scaled for auto/consumer GenAI SoCs with ongoing 2025–2026 toolchain releases.</li>
@@ -52,8 +52,8 @@
 <li><a href="#Blaize"><strong>Blaize</strong></a> — Nasdaq-listed (BZAI); GSP architecture for industrial/smart-city edge vision and GenAI.</li>
 <li><a href="#SynSense"><strong>SynSense</strong></a> — Speck/Xylo neuromorphic + event-vision stack; 2025–2026 funding/collabs for ultra-low-power edge AI.</li>
 <li><a href="#Sagence"><strong>Sagence AI</strong></a> — Analog CIM accelerators for efficient LLM/vision inference; active 2025–2026 AI-infra engagements post-rebrand.</li>
-<li><a href="#HuaweiAscend"><strong>Huawei Ascend</strong></a> — Ascend 910B/910C + Atlas 900 A3 SuperPoD (CloudMatrix384-class); 950-class on roadmap.</li>
-<li><a href="#Cambricon"><strong>Cambricon</strong></a> — Targets ~500k AI chips in 2026; Siyuan 590 / next-gen 690 messaging for cloud customers.</li>
+<li><a href="#HuaweiAscend"><strong>Huawei Ascend</strong></a> — Atlas 950 SuperPoD / Ascend 950DT (up to 8,192 NPUs) debuts globally at MWC 2026; WAIC hardware showcase.</li>
+<li><a href="#Cambricon"><strong>Cambricon</strong></a> — Siyuan 690 in mass production/delivery; targets ~500k AI chips in 2026; Q1 profit surge on cloud demand.</li>
 <li><a href="#Biren"><strong>Biren</strong></a> — Hong Kong IPO (6082.HK, Jan 2026); BR100-class GPGPU/AI accelerators.</li>
 <li><a href="#MooreThreads"><strong>Moore Threads</strong></a> — STAR Market IPO Dec 2025; MTT S4000/S5000 AI GPUs with MUSA software stack.</li>
 <li><a href="#MetaX"><strong>MetaX</strong></a> — STAR-listed (688802.SH); C500 in mass production, C600 domestic-process follow-on, MXMACA stack.</li>
@@ -121,8 +121,8 @@
 </ul>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer">NVIDIA Rubin platform</a></strong> — CES/GTC 2026 — Vera Rubin for agentic AI and large MoE; partner availability targeted H2 2026; Groq-class LPU tech described in the AI-factory stack.</li>
-<li><strong><a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">Blackwell NVL72 in volume</a></strong> — 2025–2026 — B200 / GB200 NVL72 systems are the primary train/serve platform in AI factories while Rubin ramps.</li>
+<li><strong><a href="https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Vera-Rubin-Opens-Agentic-AI-Frontier/default.aspx">Vera Rubin opens agentic AI frontier</a></strong> — GTC Mar 2026 — seven chips in full production (Vera CPU, Rubin GPU, NVLink 6, ConnectX-9, BlueField-4, Spectrum-6, Groq LPU); NVL72 on track for H2 2026 partner systems.</li>
+<li><strong><a href="https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer/">Vera Rubin POD / rack-scale systems</a></strong> — Mar 2026 — POD-scale co-design across five rack systems; CEO later reiterated “giant amounts” of Vera Rubin production incoming for H2 2026.</li>
 </ul>
 <p><a href="https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth">Hopper in-depth</a> · <a href="https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/">Hopper</a> · <a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/">Blackwell</a></p>
 
@@ -149,27 +149,28 @@
 </ul>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.amd.com/en/products/accelerators/instinct/mi350.html">Instinct MI350 Series</a></strong> — 4th Gen CDNA — MI350X/MI355X with large HBM3E and expanded low-precision support for GenAI train/infer; MI400/Helios on the 2026 roadmap.</li>
-<li><strong><a href="https://www.amd.com/en/newsroom/press-releases/2026-2-24-amd-and-meta-announce-expanded-strategic-partnersh.html">AMD–Meta strategic partnership</a></strong> — Feb 2026 — multi-generation Instinct deployments including custom MI450-class GPUs on Helios with ROCm, targeting multi-gigawatt scale.</li>
+<li><strong><a href="https://www.amd.com/en/newsroom/press-releases/2026-2-24-amd-and-meta-announce-expanded-strategic-partnersh.html">AMD–Meta 6GW Instinct partnership</a></strong> — Feb 2026 — up to 6GW multi-generation Instinct GPUs; custom MI450-class on Helios with ROCm; first-GW shipments targeted H2 2026.</li>
+<li><strong><a href="https://www.nextplatform.com/compute/2026/02/23/amd-says-helios-racks-and-mi400-series-gpus-on-track-for-2h-2026/4092199">Helios / MI400 series on track</a></strong> — 2026 — AMD says Helios racks and MI400/MI455X UALink systems remain on track for H2 2026 engineering/low-volume production alongside MI350 volume deployments.</li>
 </ul>
 
 <a name="Qualcomm"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Qualcomm_logo.png" height="40" loading="lazy" alt="Qualcomm" /></div>
-<div align="center" class="vendor-title"><h3>Qualcomm — Hexagon NPU &amp; Cloud AI</h3></div>
-<p class="vendor-blurb">Qualcomm’s AI silicon spans mobile Hexagon NPUs inside Snapdragon SoCs and Cloud AI inference accelerators for power-efficient serving. Hexagon evolved from DSP blocks into fused scalar/vector/tensor NPUs for on-device multimodal GenAI; Cloud AI 100 targets edge/cloud inference outside GPU clusters. Recent flagships (8 Gen 3 → 8 Elite) emphasize agentic and multimodal experiences at the handset.</p>
+<div align="center" class="vendor-title"><h3>Qualcomm — Hexagon NPU &amp; Dragonfly data-center AI</h3></div>
+<p class="vendor-blurb">Qualcomm’s AI silicon now spans mobile Hexagon NPUs in Snapdragon SoCs and a full data-center Dragonfly portfolio for agentic inference. Hexagon evolved from DSP blocks into fused scalar/vector/tensor NPUs for on-device GenAI; Cloud AI 100 was an earlier inference SKU. In Oct 2025 Qualcomm launched rack-scale Dragonfly AI200/AI250 inference accelerators, then at Investor Day Jun 2026 added AI300 with High Bandwidth Compute (HBC), the Dragonfly C1000 server CPU (Meta multi-generation agreement), and a multi-year data-center revenue push.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.androidauthority.com/qualcomm-snapdragon-8-elite-gen-5-3599341/">Snapdragon 8 Elite Gen 5</a></strong> — Snapdragon Summit 2025 — next Hexagon NPU (~37% faster, ~16% better perf/W vs. 8 Elite) for on-device LLMs and agentic AI; Xiaomi 17 among first devices.</li>
-<li><strong><a href="https://markvena.medium.com/snapdragon-summit-2025-qualcomms-confident-leap-into-the-ai-future-989e0acc319a">Snapdragon X2 Elite NPU</a></strong> — Sep 2025 — X2 Elite / X2 Elite Extreme PC platforms with Hexagon NPU at ~80 TOPS for Copilot+ / on-device AI laptops.</li>
+<li><strong><a href="https://sg.finance.yahoo.com/news/qualcomm-unveils-comprehensive-data-center-193000894.html">Dragonfly AI300 / data-center roadmap</a></strong> — Jun 24, 2026 Investor Day — Dragonfly AI300 (3rd-gen rack inference with HBC Gen 2; sampling ~2028) joins AI200/AI250; Dragonfly C1000 CPU (250+ Oryon cores) with multi-generation Meta server deal; HBC near-memory compute for token efficiency.</li>
+<li><strong><a href="https://www.datacenterdynamics.com/en/news/qualcomm-launches-ai200-and-ai250-chip-offering-targeting-inferencing-workloads-at-rack-scale/">Dragonfly AI200 / AI250 launch</a></strong> — Oct 2025 — Hexagon-based rack-scale inference accelerators: AI200 (~768GB LPDDR/card, commercial ~2026) and AI250 (HBC Gen 1 near-memory, ~2027); Humain among early large deploy partners.</li>
 </ul>
 
 <a name="Samsung"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/Samsung_logo.png" height="35" loading="lazy" alt="Samsung" /></div>
 <div align="center" class="vendor-title"><h3>Samsung — Exynos NPU</h3></div>
-<p class="vendor-blurb">Samsung integrates NPUs into Exynos mobile SoCs and builds memory (HBM) that underpins datacenter AI GPUs. On the compute side, Exynos NPUs have scaled with each flagship generation for vision and on-device generative AI; Exynos 2500 on 3nm GAA is the current high-water mark for Samsung’s own mobile AI silicon.</p>
+<p class="vendor-blurb">Samsung integrates NPUs into Exynos mobile SoCs and builds HBM that underpins datacenter AI GPUs. Exynos NPUs scaled with each flagship for vision and on-device GenAI; Exynos 2500 (3nm GAA) was followed by Exynos 2600 — Samsung’s first 2nm GAA mobile SoC — powering Galaxy S26 with substantially higher generative-AI NPU performance and AI graphics features (ENSS).</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://semiconductor.samsung.com/processor/mobile-processor/exynos-2500/">Exynos 2500</a></strong> — 2025 flagship on 3nm GAA; Samsung cites up to ~59 TOPS NPU and large on-device AI gains vs. Exynos 2400 for GenAI inference.</li>
+<li><strong><a href="https://semiconductor.samsung.com/processor/mobile-processor/exynos-2600/">Exynos 2600</a></strong> — First 2nm GAA mobile SoC; Samsung cites ~113% generative-AI NPU gains vs. prior gen for larger on-device models; ships in Galaxy S26 / S26+.</li>
+<li><strong><a href="https://www.asiae.co.kr/en/article/2026042810393630443">ENSS AI graphics on Exynos 2600</a></strong> — Apr 2026 — Exynos Neural Super Sampling (ENSS) commercialized for AI upscaling/frame generation to cut GPU load on flagship gaming/graphics.</li>
 </ul>
 
 <a name="MediaTek"></a>
@@ -197,7 +198,7 @@
 <p class="vendor-blurb">Broadcom co-designs customer-specific AI accelerators (XPUs) and pairs them with Tomahawk/Jericho Ethernet switching. The model began with Google TPU generations and now spans Meta MTIA, OpenAI custom silicon, and other frontier/cloud programs—positioning Broadcom as the primary ASIC implementation partner opposite merchant GPUs.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/">OpenAI–Broadcom strategic collaboration</a></strong> — Oct 2025 — multi-year plan for ~10 GW of OpenAI-designed accelerators with Broadcom; rack deployments targeted H2 2026–2029.</li>
+<li><strong><a href="https://openai.com/index/openai-broadcom-jalapeno-inference-chip/">OpenAI Jalapeño with Broadcom</a></strong> — Jun 2026 — OpenAI’s first custom LLM inference ASIC co-developed with Broadcom (Tomahawk networking); initial deployments targeted end of 2026 within multi-GW platform.</li>
 <li><strong><a href="https://www.nasdaq.com/press-release/broadcom-announces-extended-partnership-meta-deploy-technology-support-multi">Meta MTIA partnership extended</a></strong> — Apr 2026 — XPU platform continues as co-design backbone for multi-generation MTIA and Ethernet AI fabric.</li>
 </ul>
 
@@ -287,10 +288,11 @@
 <a name="Alibaba"></a>
 <div align="center" class="vendor-logo"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/alibaba_logo.png" height="60" loading="lazy" alt="Alibaba" /></div>
 <div align="center" class="vendor-title"><h3>Alibaba — T-Head PPU</h3></div>
-<p class="vendor-blurb">Alibaba’s T-Head unit designs cloud AI accelerators for Alibaba Cloud under the PPU / Zhenwu family, aiming at domestic training and inference as GPU import constraints tighten. Reported SKUs emphasize large HBM capacity and chip-to-chip bandwidth for LLM clusters in China cloud and carrier deployments.</p>
+<p class="vendor-blurb">Alibaba’s T-Head unit designs cloud AI accelerators under the Zhenwu / PPU family for Alibaba Cloud and external customers as a domestic GPU-class alternative. After Zhenwu 810E-class SKUs, Zhenwu M890 (2026) raises HBM capacity and chip-to-chip bandwidth for agentic train/infer, paired with Panjiu AL128 servers, ICN Switch networking, and the SAIL software stack.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.eetimes.com/alibaba-unveils-own-ai-chip-mounting-direct-challenge-to-nvidia/">Alibaba unveils own AI chip</a></strong> — Sep 2025 — PPU / Zhenwu targets cloud train/infer; reports cite ~96GB HBM2e-class memory and large China Unicom / Alibaba Cloud deployments.</li>
+<li><strong><a href="https://www.alibabacloud.com/blog/alibaba-unveils-new-ai-chip-flagship-model-and-rebuilt-cloud-stack-ai-for-agentic-era_603151">Zhenwu M890 AI chip</a></strong> — May 2026 Cloud Summit — M890 triples prior-gen performance claims with 144GB HBM3 and 800GB/s chip-to-chip bandwidth; FP32→FP4; powers Panjiu AL128 with ICN Switch 1.0.</li>
+<li><strong><a href="https://thenextweb.com/news/alibaba-zhenwu-m890-t-head-china-ai-chip-nvidia">T-Head scale shipments</a></strong> — 2026 — T-Head cites 560,000+ Zhenwu AI chips shipped and 400+ external customers; further V900 (2027) / J900 (2028) roadmap messaging.</li>
 </ul>
 
 <a name="Baidu"></a>
@@ -318,7 +320,8 @@
 <p class="vendor-blurb">Meta’s MTIA (Meta Training and Inference Accelerator) is an in-house family for ranking, recommendation, and increasingly generative workloads at hyperscale. Built with Broadcom as implementation partner, MTIA has moved to a rapid multi-generation chiplet cadence (MTIA 300–500 class messaging) aimed at Meta’s production AI fleet rather than merchant sale.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://ai.meta.com/blog/meta-mtia-scale-ai-chips-for-billions/">Four MTIA chips in two years</a></strong> — Multi-generation MTIA roadmap with modular chiplets and accelerating deployment cadence through 2026–2027.</li>
+<li><strong><a href="https://techcrunch.com/2026/07/09/metas-new-ai-chips-will-begin-production-in-september/">MTIA Iris production September 2026</a></strong> — Jul 2026 reporting — next-gen MTIA chip (Iris / v3) cleared testing and targets September production with Broadcom design + TSMC 3nm; part of ~6-month cadence through 2027.</li>
+<li><strong><a href="https://ai.meta.com/blog/meta-mtia-scale-ai-chips-for-billions/">Four MTIA chips in two years</a></strong> — Multi-generation MTIA roadmap (300–500 class) with modular chiplets; continues alongside AMD Instinct and NVIDIA GPU capacity.</li>
 </ul>
 
 <a name="Tesla"></a>
@@ -539,10 +542,11 @@
 &nbsp;&nbsp;
 <a href="https://www.hisilicon.com/"><img src="https://github.com/basicmi/AI-Chip/raw/master/resource/HiSilicon_logo.png" height="40" loading="lazy" alt="HiSilicon logo" /></a></div>
 <div align="center" class="vendor-title"><h3>Huawei Ascend / HiSilicon</h3></div>
-<p class="vendor-blurb">Huawei’s Ascend line (HiSilicon) is China’s most widely deployed domestic training/inference accelerator family. Ascend 910 established the high-end; 910B/910C scaled under export controls into Atlas servers and SuperPoD / CloudMatrix supernodes. Cann/MindSpore software and Atlas systems form a full stack for China large-model train and serve; Ascend 950-class follow-ons are on the roadmap.</p>
+<p class="vendor-blurb">Huawei’s Ascend line (HiSilicon) is China’s most widely deployed domestic training/inference accelerator family. Ascend 910 established the high-end; 910B/910C scaled under export controls into Atlas servers and SuperPoD / CloudMatrix supernodes. Ascend 950-class (950DT training / 950PR inference messaging) and Atlas 950 SuperPoD push UnifiedBus optical scale-up to thousands of NPUs as a single logical node, with Cann/MindSpore as the software stack.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.huawei.com/en/news/2025/9/hc-xu-keynote-speech">Atlas 900 A3 SuperPoD / Ascend 910C</a></strong> — 2025 — scaled 910B/910C deployments; Atlas 900 A3 SuperPoD (up to 384× Ascend 910C) / CloudMatrix384-class supernodes; Ascend 950-class on roadmap.</li>
+<li><strong><a href="https://www.huawei.com/en/news/2026/3/mwc-superpod-ai">Atlas 950 SuperPoD at MWC 2026</a></strong> — Mar 2026 — global debut of Atlas 950 SuperPoD on UnifiedBus; scales from 64 NPUs/cabinet toward up to 8,192 Ascend 950-class NPUs for large-model train/infer.</li>
+<li><strong><a href="https://it-news.uk/posts/huawei-atlas-950-superpod-waic-2026/">Atlas 950 SuperPoD at WAIC 2026</a></strong> — Jul 2026 — first public hardware demonstration planned at WAIC Shanghai; single-cabinet 64-card base with global unified memory addressing for trillion-parameter models.</li>
 </ul>
 
 <a name="Cambricon"></a>
@@ -551,7 +555,8 @@
 <p class="vendor-blurb">Cambricon is China’s leading pure-play AI-chip vendor, spanning edge IP/SoCs and cloud MLU / Siyuan accelerators. After an early edge-IP era, the company refocused on cloud training/inference cards for Chinese internet and intelligent-computing customers, with aggressive 2026 volume plans.</p>
 <p class="latest-label"><strong>Latest</strong></p>
 <ul class="latest-list">
-<li><strong><a href="https://www.tomshardware.com/tech-industry/semiconductors/cambricon-targets-500000-ai-chips-in-2026-as-china-accelerates-domestic-hardware-push">500,000 AI chips target for 2026</a></strong> — Aggressive 2026 volume plans for Siyuan/MLU-class accelerators (Siyuan 590 and next-gen 690 messaging) for Chinese cloud/internet customers.</li>
+<li><strong><a href="https://en.shuziqushi.com/new343300.html">Siyuan 690 mass production</a></strong> — 2026 — next-gen Siyuan/MLU 690 cloud accelerators in mass production/delivery (reports: dual-die, ~196GB HBM3, &gt;700 TFLOPS FP16-class); Q1 profit surged on domestic cloud demand.</li>
+<li><strong><a href="https://www.tomshardware.com/tech-industry/semiconductors/cambricon-targets-500000-ai-chips-in-2026-as-china-accelerates-domestic-hardware-push">500,000 AI chips target for 2026</a></strong> — Aggressive 2026 volume plans for Siyuan 590/690-class accelerators serving Chinese internet/cloud customers as a leading pure-play domestic AI-chip vendor.</li>
 </ul>
 
 <a name="Biren"></a>