docs: whitepaper -- full backprop trains XOR on live silicon (capstone) - #1887
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The full 2-layer backprop microsequencer now trains XOR to 4/4 on a live Artix-7 (25/25 epochs, both layers learning on-chip, weight trajectory bit-exact to the independent Python model). Updated the whitepaper: S3 Training adds the capstone; S4(b) changes 'trains XOR to 4/4 ... bitstream built and validated' to 'flashed to a real Artix-7 and trains XOR to 4/4 across 25/25 epochs, both layers learning on-chip, bit-exact to the model'; S5 honesty resolves the 'pending JTAG re-connect' item and replaces it with the seed-search caveat (nextpnr-xilinx can't express a multicycle constraint, so the deep path is placement-dependent -- pick a stable seed; an open-toolchain limitation a commercial P&R would close directly). Docs only. Refs #1764 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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The full 2-layer backprop microsequencer now trains XOR to 4/4 on a live Artix-7 (25/25 epochs, both layers learning on-chip, weight trajectory bit-exact to the independent Python model — ep0 0.000/0.551/0.936/0.232 == model). Updated the whitepaper: S3 Training adds the capstone; S4(b) now states 'flashed to a real Artix-7 and trains XOR to 4/4 across 25/25 epochs, both layers learning on-chip, bit-exact to the model' (a full forward+loss+backward+update loop on live silicon); S5 honesty resolves the 'pending JTAG re-connect' item and replaces it with the honest seed-search caveat (nextpnr-xilinx can't express a multicycle constraint → the deep shared-core path is placement-dependent; pick a stable seed — an open-toolchain limitation a commercial P&R would close directly). Docs only. Refs #1764