applied/opt09_srif_softreset/reset_x_bits_full.summary.json

{
  "key": "opt09_srif_softreset/reset_x_bits_full",
  "tag": "reset_x_bits_full",
  "script": "opt09_srif_softreset.py",
  "script_sha256_16": "dd62e980e1fe3fd1",
  "hf_folder": "applied/opt09_srif_softreset",
  "priority": 2,
  "note": "the full reset-rule x precision grid in srif_sweep.json",
  "phase": 10,
  "n_phases": 10,
  "method": "harmonic_matched",
  "command": [
    "/usr/bin/python3",
    "-m",
    "torch.distributed.run",
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    "/kaggle/working/scripts/opt09_srif_softreset.py",
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    "--data-root",
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    "--hub-dir",
    "/kaggle/working/hub/opt09_srif_softreset/reset_x_bits_full",
    "--keep-epoch-weights",
    "1",
    "--resplit",
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    "--bits-sweep",
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  "started_utc": "2026-08-23T05:30:36.327230+00:00",
  "finished_utc": "2026-08-23T05:36:29.115302+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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            "acc_by_bits": {
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              "4": 0.8342857142857143,
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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": {
      "method": "harmonic_matched",
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