update APPLIED run index

applied/_index/APPLIED_RUNS_INDEX.json

@@ -7018,5 +7018,316 @@
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+    "tag": "u_headroom_1.0",
+    "script": "opt09_srif_softreset.py",
+    "hf_folder": "applied/opt09_srif_softreset",
+    "phase": 10,
+    "status": "ok",
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+    "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,
+      "method": "harmonic_matched",
+      "accuracy": {
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+        "best_val_acc": 0.9271428571428572
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+        "reset_x_bits_sweep": {
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+              "acc": 0.027142857142857142,
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+            {
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+              "acc": 0.02642857142857143,
+              "target_acc": 0.02642857142857143,
+              "grid_headroom": 1.0,
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+          "by_mode": {
+            "hard": {
+              "fp32_acc": 0.025,
+              "acc_by_bits": {
+                "0": 0.025,
+                "4": 0.025,
+                "2": 0.025
+              }
+            },
+            "soft": {
+              "fp32_acc": 0.025,
+              "acc_by_bits": {
+                "0": 0.025,
+                "4": 0.025,
+                "2": 0.025
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+            "trained": {
+              "fp32_acc": 0.027142857142857142,
+              "acc_by_bits": {
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+                "4": 0.027142857142857142,
+                "2": 0.02642857142857143
+              }
+            }
+          }
+        },
+        "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,
+        "drop_rest": true,
+        "resplit": "block",
+        "test_blocks": "6",
+        "class_weight": false,
+        "epochs": 60,
+        "batch_size": 64,
+        "lr": 0.002,
+        "weight_decay": 0.0005,
+        "grad_clip": 5.0,
+        "num_bins": 100,
+        "window_sec": 5.0,
+        "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/u_headroom_1.0",
+        "repo_id": "UWU-R-13/SSVEP-SNN",
+        "push_hf": false,
+        "keep_epoch_weights": 1,
+        "no_resume": true,
+        "selftest": false,
+        "reset_mode": "srif",
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+        "lam_init": 0.9,
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+        "u_bits": 4,
+        "u_headroom": 1.0,
+        "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
+      }
+    },
+    "zip": {
+      "name": "u_headroom_1.0.zip",
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+      "sha256": "bdc988664ace513f808a1701b953a16837db9aae49686a1a8ebbda310392ac05"
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   }
 ]