update APPLIED run index
Browse files- applied/_index/APPLIED_RUNS_INDEX.json +632 -634
applied/_index/APPLIED_RUNS_INDEX.json
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@@ -1,106 +1,245 @@
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[
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{
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"key": "
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"tag": "
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"script": "
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"hf_folder": "applied/
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"phase":
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"status": "ok",
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"duration_s":
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"optimization_summary":
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"zip": {
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"name": "
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"files":
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"bytes":
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"MB": 12.
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"sha256": "
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},
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{
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"key": "opt01_ttfs_dta/
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"tag": "
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"script": "opt01_ttfs_dta.py",
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"hf_folder": "applied/opt01_ttfs_dta",
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"phase":
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"status": "ok",
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"duration_s":
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"optimization_summary": {
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"optimization": "ttfs_dta",
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"research_section": "2. Temporal-Dimension Reduction (TTFS coding / DTA-TTFS)",
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"reference": "Software-Level Optimizations for Spiking Neural Network Inference on ARM Architectures: A Deep-Dive into the Jetson Nano Ecosystem (research.md SS2)",
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"script": "opt01_ttfs_dta.py",
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"baseline_mode":
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"method": "harmonic_matched",
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"accuracy": {
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"ttfs":
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"dta":
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"ttfs_halt_k": 1,
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"dta_target_rate":
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"dta_momentum":
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"spikes_per_neuron_per_inference":
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"ttfs_budget_respected":
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"frac_neurons_that_ever_fire": 0.
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"mean_first_spike_tick":
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{
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"layer": 0,
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"neurons": 320,
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"spikes_per_neuron_per_inference":
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"mean_first_spike_tick":
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{
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"layer": 1,
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"neurons": 512,
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"spikes_per_neuron_per_inference":
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"mean_first_spike_tick":
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{
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"layer": 2,
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"neurons": 256,
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"spikes_per_neuron_per_inference":
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"frac_neurons_that_ever_fire": 0.
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"mean_first_spike_tick":
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{
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"layer": 3,
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"neurons": 40,
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"spikes_per_neuron_per_inference":
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"frac_neurons_that_ever_fire": 0.
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"mean_first_spike_tick":
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}
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"mean_halt_tick":
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"effective_ticks":
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"ticks_saved_frac": 0.
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"num_ticks": 100,
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"dta_thr_min":
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"dta_thr_max":
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"dta_thr_mean":
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"base_thresh":
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"config": {
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"method": "harmonic_matched",
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"no_batchnorm": false,
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"binary_input": false,
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"beta": 1.0,
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"thresh":
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"window": 0.5,
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"gain": 1.0,
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"alpha": 0.9,
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"decision_margin": 2.0,
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"export": false,
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"seed": 1234,
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"hub_dir": "/kaggle/working/hub/opt01_ttfs_dta/
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"repo_id": "UWU-R-13/SSVEP-SNN",
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"push_hf": false,
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"keep_epoch_weights": 1,
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"no_resume": true,
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"selftest": false,
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"baseline":
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"bench": true,
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"bench_warmup": 10,
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"bench_iters": 50,
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@@ -154,7 +293,7 @@
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"ttfs": true,
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"ttfs_halt_k": 1,
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"dta": true,
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"dta_target_rate": 0.
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"dta_readout_rate": 0.95,
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"ttfs_logit_scale": 10.0,
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"dta_momentum": 0.1,
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}
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},
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"zip": {
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"name": "
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"bytes":
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"MB":
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"sha256": "
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}
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},
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{
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"key": "opt01_ttfs_dta/
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"tag": "
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"script": "opt01_ttfs_dta.py",
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"hf_folder": "applied/opt01_ttfs_dta",
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"phase":
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"status": "ok",
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"duration_s":
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"optimization_summary": {
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"optimization": "ttfs_dta",
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"research_section": "2. Temporal-Dimension Reduction (TTFS coding / DTA-TTFS)",
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"baseline_mode": false,
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"method": "harmonic_matched",
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"accuracy": {
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"efficiency": {
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"spikes_per_inference": null,
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"weight_sparsity": 0.7324707846410685,
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"effective_ticks": 100.0,
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"num_ticks": 100,
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"membrane_bytes_per_inference": 451200,
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"weight_bytes": 4907008,
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"input_raster_bytes": null,
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"peak_vram_mb": 2909.
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"optimization_specific": {
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"ttfs": true,
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"dta": true,
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"readout": "ttfs",
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"ttfs_halt_k": 1,
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"dta_target_rate": 0.
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"dta_momentum": 0.1,
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"spikes_per_neuron_per_inference": 0.
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"ttfs_budget_respected": true,
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"frac_neurons_that_ever_fire": 0.
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"mean_first_spike_tick":
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"per_layer_ttfs": [
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{
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"layer": 0,
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"neurons": 320,
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"spikes_per_neuron_per_inference": 0.
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"mean_first_spike_tick":
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{
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"layer": 1,
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"neurons": 512,
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"spikes_per_neuron_per_inference": 0.
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"frac_neurons_that_ever_fire": 0.
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"mean_first_spike_tick":
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{
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"layer": 2,
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"neurons": 256,
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"spikes_per_neuron_per_inference": 0.
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"frac_neurons_that_ever_fire": 0.
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"mean_first_spike_tick":
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{
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"layer": 3,
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"neurons": 40,
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"spikes_per_neuron_per_inference": 0.
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"frac_neurons_that_ever_fire": 0.
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"mean_first_spike_tick":
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}
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],
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"mean_halt_tick":
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"effective_ticks":
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"ticks_saved_frac": 0.
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"num_ticks": 100,
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"dta_thr_min": 10.0,
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"dta_thr_max":
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"dta_thr_mean":
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"base_thresh": 10.0
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},
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"config": {
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"decision_margin": 2.0,
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"export": false,
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"seed": 1234,
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"hub_dir": "/kaggle/working/hub/opt01_ttfs_dta/
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"repo_id": "UWU-R-13/SSVEP-SNN",
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"push_hf": false,
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"keep_epoch_weights": 1,
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"ttfs": true,
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"ttfs_halt_k": 1,
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"dta": true,
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"dta_target_rate": 0.
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"dta_readout_rate": 0.95,
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"ttfs_logit_scale": 10.0,
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"dta_momentum": 0.1,
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}
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},
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"zip": {
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"name": "
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"files": 80,
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"bytes":
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"MB": 12.
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"sha256": "
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{
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"key": "
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"tag": "
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"script": "
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"hf_folder": "applied/
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"phase":
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"status": "ok",
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"duration_s":
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"optimization_summary": {
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"optimization": "
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"research_section": "2. Temporal-
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"reference": "
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"script": "
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"baseline_mode":
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"method": "harmonic_matched",
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"accuracy": {
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"efficiency": {
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"spikes_per_inference": null,
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"dense_macs_per_inference": 122675200.0,
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"weight_sparsity": 0.7324707846410685,
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"num_ticks": 100,
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"membrane_bytes_per_inference": 451200,
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"weight_bytes": 4907008,
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"optimization_specific": {
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},
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"config": {
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"method": "harmonic_matched",
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"no_batchnorm": false,
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"binary_input": false,
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"beta": 1.0,
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"thresh":
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"window": 0.5,
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"alpha": 0.9,
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"fake_quant": 0,
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"amp": false,
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"spike_reg": 0.0,
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"decision_margin": 2.0,
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"export": false,
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"seed": 1234,
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"bench": true,
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"bench_iters": 50,
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"bench_train_steps": 20,
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"anytime_fracs": "0.25,0.5,1.0",
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"anytime_weights": "0.3,0.3,1.0",
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"ic_layer": 0,
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"ic_loss_weight": 0.3,
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"bayes_fusion": false,
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"conf_threshold": 0.9,
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"conf_temp": 2.0,
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