applied/opt09_srif_softreset/lam_init_0.1.summary.json

{
  "key": "opt09_srif_softreset/lam_init_0.1",
  "tag": "lam_init_0.1",
  "script": "opt09_srif_softreset.py",
  "script_sha256_16": "dd62e980e1fe3fd1",
  "hf_folder": "applied/opt09_srif_softreset",
  "priority": 1,
  "note": "near hard reset",
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  "n_phases": 10,
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  "command": [
    "/usr/bin/python3",
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    "/kaggle/working/scripts/opt09_srif_softreset.py",
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    "--keep-epoch-weights",
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    "--lam-init",
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  "flags": [
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  "exit_code": 0,
  "status": "ok",
  "duration_s": 363.2,
  "started_utc": "2026-08-23T03:54:29.846734+00:00",
  "finished_utc": "2026-08-23T04:00:33.031167+00:00",
  "optimization_summary": {
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    "research_section": "4. Membrane-Potential Memory Wall -- Soft Reset Integrate-and-Fire (SRIF), dynamic reset potential",
    "reference": "Guo et al., 'Reducing Information Loss for Spiking Neural Networks' (SRIF), ECCV 2022; research.md S4 (stacks with MINT / SpQuant-SNN membrane quantization)",
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      },
      "mechanism": "V = u - s*((1-lam)*u + lam*thresh) + s*v_r; lam=0,v_r=0 is the baseline hard reset exactly, lam=1,v_r=0 discards zero charge",
      "cost": "2 scalars per spiking layer; identical FMA count per tick, so the reset change is free at inference time",
      "caveat": "the sweep swaps the reset on ALREADY-TRAINED weights, which under-states what training with each reset would give; and a charge-conserving reset can raise the spike count, so read spikes_per_inference next to the accuracy"
    },
    "config": {
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