{
  "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",
  "phase": 5,
  "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/lam_init_0.1",
    "--keep-epoch-weights",
    "1",
    "--resplit",
    "block",
    "--drop-rest",
    "--bench",
    "--lam-init",
    "0.1"
  ],
  "flags": [
    "--lam-init",
    "0.1"
  ],
  "epochs_requested": 60,
  "gpus": 2,
  "relative_epoch_cost": 1.0,
  "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": {
    "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",
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    "method": "harmonic_matched",
    "accuracy": {
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    },
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      "effective_ticks": 21.771428571428572,
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      "membrane_bytes_per_inference": 451200,
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      "input_raster_bytes": null,
      "train_ms_per_step": 104.55192480001188,
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      "reset_family": {
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        "num_spiking_sites": 4,
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      "reset_stats": {
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      "reset_x_bits_sweep": {
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            "target_acc": 0.7571428571428571,
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          {
            "reset_mode": "trained",
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            "target_acc": 0.8864285714285715,
            "grid_headroom": 0.0,
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            "discarded_charge_per_reset": -0.51547552848815,
            "spikes_per_inference": 13885.140625,
            "membrane_saturation_frac": null,
            "membrane_memory_reduction_x": 1.0,
            "acc_drop_vs_fp32": 0.0
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          {
            "reset_mode": "trained",
            "u_bits": 4,
            "acc": 0.9092857142857143,
            "target_acc": 0.9092857142857143,
            "grid_headroom": 1.75,
            "grid_levels_above_thresh": 3,
            "discarded_charge_per_reset": -0.5824071496424404,
            "spikes_per_inference": 17677.2734375,
            "membrane_saturation_frac": 0.3281896193484043,
            "membrane_memory_reduction_x": 8.0,
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          {
            "reset_mode": "trained",
            "u_bits": 2,
            "acc": 0.06642857142857143,
            "target_acc": 0.06642857142857143,
            "grid_headroom": 1.0,
            "grid_levels_above_thresh": 0,
            "discarded_charge_per_reset": -0.6881111096608943,
            "spikes_per_inference": 24211.9453125,
            "membrane_saturation_frac": 0.4429418079565603,
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        ],
        "by_mode": {
          "hard": {
            "fp32_acc": 0.6842857142857143,
            "acc_by_bits": {
              "0": 0.6842857142857143,
              "4": 0.6864285714285714,
              "2": 0.16214285714285714
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          },
          "soft": {
            "fp32_acc": 0.7814285714285715,
            "acc_by_bits": {
              "0": 0.7814285714285715,
              "4": 0.7571428571428571,
              "2": 0.16214285714285714
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          },
          "trained": {
            "fp32_acc": 0.8864285714285715,
            "acc_by_bits": {
              "0": 0.8864285714285715,
              "4": 0.9092857142857143,
              "2": 0.06642857142857143
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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": "",
      "limit": 0,
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      "hidden": "512,256",
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      "dropout": 0.3,
      "spike_drop": 0.1,
      "time_jitter": 2,
      "label_smoothing": 0.05,
      "no_batchnorm": false,
      "binary_input": false,
      "beta": 1.0,
      "thresh": 1.0,
      "window": 0.5,
      "gain": 1.0,
      "alpha": 0.9,
      "readout": "spikecount",
      "fake_quant": 0,
      "amp": false,
      "spike_reg": 0.0,
      "event_eval": true,
      "decision_margin": 2.0,
      "export": false,
      "seed": 1234,
      "hub_dir": "/kaggle/working/hub/opt09_srif_softreset/lam_init_0.1",
      "repo_id": "UWU-R-13/SSVEP-SNN",
      "push_hf": false,
      "keep_epoch_weights": 1,
      "no_resume": true,
      "selftest": false,
      "reset_mode": "srif",
      "learn_reset": true,
      "lam_init": 0.1,
      "v_reset_init": 0.0,
      "u_bits": 0,
      "u_headroom": 2.0,
      "reset_sweep": "hard,soft,trained",
      "bits_sweep": "0,4,2",
      "baseline": false,
      "bench": true,
      "bench_warmup": 10,
      "bench_iters": 50,
      "bench_train_steps": 20,
      "sampling_rate": 250.0
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    "final_test_rest_recall": NaN,
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  "extra_sweep_file_present": true,
  "selftest": {
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