From 546e8c39f3654b0af5c026ae46dcd7d24336ca38 Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Sat, 29 Aug 2026 20:43:29 -0500 Subject: document confirmed digital scaling evidence --- README.md | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) (limited to 'README.md') diff --git a/README.md b/README.md index 6d4204d..9cae3cb 100644 --- a/README.md +++ b/README.md @@ -27,9 +27,10 @@ three experimental parts are: 1. add the same innovation subtraction to digital Dual Propagation, Equilibrium Propagation, standard coupled learning, and overclamped coupled learning; -2. scale a digital CLLN from 32 to 2,048 learnable edges under fixed component - imperfection, comparing clean learning, same-RMS zero-mean noise, raw - imperfection, static calibration, and SDIL; +2. scale a digital Contrastive Local Learning Network (CLLN) from 32 to 2,048 + learnable edges under fixed component imperfection, comparing clean + learning, same-RMS zero-mean noise, raw imperfection, static calibration, + and SDIL; 3. test the update in the nonlinear CLLN with published component-error scales, nonideal local correlated-double-sampling, and a SPICE check of the local sample/hold/subtract primitive. @@ -49,16 +50,20 @@ The completed transfer evidence is: Dual Propagation recovers from 9.40% to 82.92% validation accuracy against 82.86% clean; EP recovers from 31.38% to 74.52% against 76.26% clean; and the nonlinear 4-by-4 CLLN recovers from 25.42% mean classification error to 0% on 120 untouched task/device pairs. -The first fully confirmed digital -CLLN size contains 40 tasks and three device draws: at 32 edges, clean and -SDIL have 0% error, raw imperfection has 34.69%, same-RMS noise has 0.73%, and -static calibration has 6.15%. The remaining five sizes are running under the -frozen protocol. +The confirmed digital CLLN ladder contains 40 tasks, three device draws, six +sizes, and five methods. From 32 to 2,048 edges, static-calibration error grows +from 6.15% to 27.19%, while SDIL grows from 0% to 2.29% against 1.56% clean at +the largest size. SDIL reduces the excess final-error growth slope by 96.2% +and stable-failure growth by 89.5% relative to static calibration. At 2,048 +edges it uses 23.3% fewer local updates to the censored target but 52.8% more +local scalar reads because each update includes a neutral measurement. Start with [`THREE_PART_EVIDENCE.md`](THREE_PART_EVIDENCE.md) for the evidence map and [`CLLN_SCALING.md`](CLLN_SCALING.md) for the frozen scaling protocol. The current hardware-realistic figure is [`figure_physical_hardware_evidence_confirmation.pdf`](results/figs/figure_physical_hardware_evidence_confirmation.pdf). +The confirmed digital scaling figure is +[`figure_clln_scaling_confirmation.pdf`](results/figs/figure_clln_scaling_confirmation.pdf). The standard-ResNet stability branch additionally uses a fast paired neutral observation to project any *remaining* affine neutral residual off current -- cgit v1.2.3