docs: GF-T technical whitepaper (spec-first ternary NN that trains on silicon) - #1866
Merged
Conversation
… silicon) docs/GFT_WHITEPAPER.md: consolidated, honest technical whitepaper for the investor round / due-diligence. Thesis, the GF-T format, 14 silicon-proven bitstreams (inference + training + edge loop), the two scaling results (~2x magsub optimization proven over 17.4M pairs; microsequencer full backprop at near-constant area = a programmable ternary NN trainer), measured limits, the open-source pipeline, and competitive positioning (all competitors inference-only). Refs #1764 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Contributor
Contributor
|
📓 NotebookLM Notebook linked to this PR
This notebook contains session context, decisions, and artifacts for this work. |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
docs/GFT_WHITEPAPER.md: consolidated, honest technical whitepaper for the investor round. Thesis, GF-T format, 14 silicon-proven bitstreams (inference + training + edge loop), the two scaling results (~2x magsub optimization proven over 17.4M pairs; microsequencer = programmable ternary NN trainer at near-constant area), measured limits (openXC7 ~17M ceiling), open-source pipeline, competitive positioning. docs/NOW.md updated. Refs #1764