applied/opt09_srif_softreset/v_reset_init_neg.summary.json

{
  "key": "opt09_srif_softreset/v_reset_init_neg",
  "tag": "v_reset_init_neg",
  "script": "opt09_srif_softreset.py",
  "script_sha256_16": "dd62e980e1fe3fd1",
  "hf_folder": "applied/opt09_srif_softreset",
  "priority": 2,
  "note": "resting potential below zero: a refractory-like dip after each spike",
  "phase": 10,
  "n_phases": 10,
  "method": "harmonic_matched",
  "command": [
    "/usr/bin/python3",
    "-m",
    "torch.distributed.run",
    "--standalone",
    "--nproc_per_node=2",
    "/kaggle/working/scripts/opt09_srif_softreset.py",
    "--method",
    "harmonic_matched",
    "--data-root",
    "/kaggle/working/data",
    "--epochs",
    "60",
    "--seed",
    "1234",
    "--batch-size",
    "64",
    "--event-eval",
    "--no-resume",
    "--decision-margin",
    "2.0",
    "--hub-dir",
    "/kaggle/working/hub/opt09_srif_softreset/v_reset_init_neg",
    "--keep-epoch-weights",
    "1",
    "--resplit",
    "block",
    "--drop-rest",
    "--bench",
    "--v-reset-init",
    "-0.5"
  ],
  "flags": [
    "--v-reset-init",
    "-0.5"
  ],
  "epochs_requested": 60,
  "gpus": 2,
  "relative_epoch_cost": 1.0,
  "exit_code": 0,
  "status": "ok",
  "duration_s": 341.6,
  "started_utc": "2026-08-23T05:59:23.800991+00:00",
  "finished_utc": "2026-08-23T06:05:05.357868+00:00",
  "optimization_summary": {
    "optimization": "srif_softreset",
    "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)",
    "script": "opt09_srif_softreset.py",
    "baseline_mode": false,
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    "accuracy": {
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    "optimization_specific": {
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      "reset_family": {
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      "reset_stats": {
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      "reset_x_bits_sweep": {
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            "reset_mode": "trained",
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          {
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          {
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        ],
        "by_mode": {
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            "fp32_acc": 0.8028571428571428,
            "acc_by_bits": {
              "0": 0.8028571428571428,
              "4": 0.8607142857142858,
              "2": 0.605
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          },
          "soft": {
            "fp32_acc": 0.8492857142857143,
            "acc_by_bits": {
              "0": 0.8492857142857143,
              "4": 0.8735714285714286,
              "2": 0.605
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          "trained": {
            "fp32_acc": 0.86,
            "acc_by_bits": {
              "0": 0.86,
              "4": 0.9085714285714286,
              "2": 0.5392857142857143
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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",
      "data_root": "/kaggle/working/data",
      "subjects": "",
      "blocks": "",
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      "repo_id": "UWU-R-13/SSVEP-SNN",
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      "sampling_rate": 250.0
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    "final_test_rest_recall": NaN,
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  "extra_sweep_file_present": true,
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