p-j-r-1-2-3 commited on
Commit
7af3b80
·
verified ·
1 Parent(s): e0e3ae0

update APPLIED run index

Browse files
Files changed (1) hide show
  1. applied/_index/APPLIED_RUNS_INDEX.json +465 -596
applied/_index/APPLIED_RUNS_INDEX.json CHANGED
@@ -461,258 +461,139 @@
461
  }
462
  },
463
  {
464
- "key": "opt02_seenn_bayes/default",
465
- "tag": "default",
466
- "script": "opt02_seenn_bayes.py",
467
- "hf_folder": "applied/opt02_seenn_bayes",
468
- "phase": 1,
469
  "status": "ok",
470
- "duration_s": 586.1,
471
  "optimization_summary": {
472
- "optimization": "seenn_early_exit_bayesian_fusion",
473
- "research_section": "2. Temporal-dimension reduction",
474
- "reference": "SEENN: Towards Temporal Spiking Early-Exit Neural Networks, arXiv:2304.01230 (NeurIPS 2023); BayesianSpikeFusion, Front. Neurosci. 2024 (PMC11330889)",
475
- "script": "opt02_seenn_bayes.py",
476
  "baseline_mode": false,
477
  "method": "harmonic_matched",
478
  "accuracy": {
479
- "test_acc": 0.9278571428571428,
480
- "test_target_acc": 0.9278571428571428,
481
- "best_val_acc": 0.9307142857142857
482
  },
483
  "efficiency": {
484
  "spikes_per_inference": null,
485
- "syn_ops_per_inference": 3638362.4326298703,
486
- "dense_macs_per_inference": 123955200.0,
487
- "event_fraction_of_dense": 0.029352237200455246,
488
- "syn_ops_per_inference_dense_fanout": 65214980.21915584,
489
  "weight_sparsity": 0.7324707846410685,
490
- "effective_ticks": 7.985,
491
  "num_ticks": 100,
492
  "membrane_bytes_per_inference": 451200,
493
  "weight_bytes": 4907008,
494
  "input_raster_bytes": null,
495
- "train_ms_per_step": 174.30670261383057,
496
- "infer_ms_per_sample": 0.6237456846476799,
497
- "peak_vram_mb": 10914.396672,
498
- "syn_ops_per_inference_early_exit": 1846599.2897727273
499
  },
500
  "optimization_specific": {
501
- "seenn_enabled": true,
502
- "bayes_fusion_enabled": true,
503
- "conf_threshold": 0.9,
504
- "conf_temp": 2.0,
505
- "anytime_fracs": "0.25,0.5,1.0",
506
- "anytime_weights": "0.3,0.3,1.0",
507
- "ic_loss_weight": 0.3,
508
- "ic_layer": 0,
509
- "mean_exit_tick": 7.985,
510
- "mean_exit_ms": 399.25000000000006,
511
- "ticks_saved_frac": 0.92015,
512
- "exit_tick_histogram": {
513
- "counts": [
514
- 1040,
515
- 316,
516
- 39,
517
- 4,
518
- 1,
519
- 0,
520
- 0,
521
- 0,
522
- 0,
523
- 0
524
- ],
525
- "edges": [
526
- 0.0,
527
- 10.0,
528
- 20.0,
529
- 30.0,
530
- 40.0,
531
- 50.0,
532
- 60.0,
533
- 70.0,
534
- 80.0,
535
- 90.0,
536
- 100.0
537
- ]
538
- },
539
- "acc_fc_only": 0.9278571428571428,
540
- "acc_ic_only": 0.935,
541
- "acc_fused": 0.9378571428571428,
542
- "acc_early_exit": 0.3578571428571429,
543
- "syn_ops_saved_frac": 0.49246417200994075,
544
- "syn_per_inference_early_exit": 1846599.2897727273,
545
- "anytime_acc": {
546
- "0.25": 0.6228571428571429,
547
- "0.5": 0.8078571428571428,
548
- "0.75": 0.8885714285714286,
549
- "1.0": 0.9278571428571428
550
- },
551
- "pareto": [
552
  {
553
- "conf": 0.5,
554
- "mean_ticks": 4.405,
555
- "acc": 0.22785714285714287,
556
- "syn_ops": 1715995.05762987
 
 
557
  },
558
  {
559
- "conf": 0.7,
560
- "mean_ticks": 5.097857142857142,
561
- "acc": 0.25285714285714284,
562
- "syn_ops": 1744549.8368506492
 
 
563
  },
564
  {
565
- "conf": 0.8,
566
- "mean_ticks": 5.984285714285714,
567
- "acc": 0.28285714285714286,
568
- "syn_ops": 1779479.5024350649
 
 
569
  },
570
  {
571
- "conf": 0.9,
572
- "mean_ticks": 7.984285714285714,
573
- "acc": 0.3578571428571429,
574
- "syn_ops": 1846555.8522727273
 
 
575
  },
576
  {
577
- "conf": 0.95,
578
- "mean_ticks": 10.01142857142857,
579
- "acc": 0.42857142857142855,
580
- "syn_ops": 1902723.0324675327
 
 
581
  },
582
  {
583
- "conf": 0.99,
584
- "mean_ticks": 14.458571428571428,
585
- "acc": 0.5621428571428572,
586
- "syn_ops": 2004484.2800324673
 
 
587
  }
588
- ]
589
- },
590
- "config": {
591
- "method": "harmonic_matched",
592
- "data_root": "/kaggle/working/data",
593
- "subjects": "",
594
- "blocks": "",
595
- "limit": 0,
596
- "drop_rest": true,
597
- "resplit": "block",
598
- "test_blocks": "6",
599
- "class_weight": false,
600
- "epochs": 60,
601
- "batch_size": 64,
602
- "lr": 0.002,
603
- "weight_decay": 0.0005,
604
- "grad_clip": 5.0,
605
- "num_bins": 100,
606
- "window_sec": 5.0,
607
- "hidden": "512,256",
608
- "freq_groups": 8,
609
- "dropout": 0.3,
610
- "spike_drop": 0.1,
611
- "time_jitter": 2,
612
- "label_smoothing": 0.05,
613
- "no_batchnorm": false,
614
- "binary_input": false,
615
- "beta": 1.0,
616
- "thresh": 1.0,
617
- "window": 0.5,
618
- "gain": 1.0,
619
- "alpha": 0.9,
620
- "readout": "spikecount",
621
- "fake_quant": 0,
622
- "amp": false,
623
- "spike_reg": 0.0,
624
- "event_eval": true,
625
- "decision_margin": 2.0,
626
- "export": false,
627
- "seed": 1234,
628
- "baseline": false,
629
- "bench": true,
630
- "bench_warmup": 10,
631
- "bench_iters": 50,
632
- "bench_train_steps": 20,
633
- "anytime_fracs": "0.25,0.5,1.0",
634
- "anytime_weights": "0.3,0.3,1.0",
635
- "ic_layer": 0,
636
- "ic_loss_weight": 0.3,
637
- "bayes_fusion": true,
638
- "fusion_prior_weight": 1.0,
639
- "conf_threshold": 0.9,
640
- "conf_temp": 2.0,
641
- "conf_sweep": "0.5,0.7,0.8,0.9,0.95,0.99",
642
- "hub_dir": "/kaggle/working/hub/opt02_seenn_bayes/default",
643
- "repo_id": "UWU-R-13/SSVEP-SNN",
644
- "push_hf": false,
645
- "keep_epoch_weights": 1,
646
- "no_resume": true,
647
- "selftest": false,
648
- "sampling_rate": 250.0
649
- }
650
- },
651
- "zip": {
652
- "name": "default.zip",
653
- "files": 81,
654
- "bytes": 12636571,
655
- "MB": 12.6,
656
- "sha256": "1c64c6704af63b5eec37b2f1d2e824ba1c5c3c28328c5b1c03089bd12b4cc747"
657
- }
658
- },
659
- {
660
- "key": "opt03_ftbc_threshold_balance/control_baseline",
661
- "tag": "control_baseline",
662
- "script": "opt03_ftbc_threshold_balance.py",
663
- "hf_folder": "applied/opt03_ftbc_threshold_balance",
664
- "phase": 1,
665
- "status": "ok",
666
- "duration_s": 321.7,
667
- "optimization_summary": {
668
- "optimization": "ftbc_threshold_balance",
669
- "research_section": "2. Temporal-Dimension Reduction (threshold balancing + FTBC)",
670
- "reference": "Rueckauer et al. arXiv:1612.04052; Sengupta et al. arXiv:1802.02627; FTBC: Forward Temporal Bias Correction, ECCV 2024",
671
- "script": "opt03_ftbc_threshold_balance.py",
672
- "baseline_mode": true,
673
- "method": "harmonic_matched",
674
- "accuracy": {
675
- "test_acc": 0.9421428571428572,
676
- "test_target_acc": 0.9421428571428572,
677
- "best_val_acc": 0.945
678
- },
679
- "efficiency": {
680
- "spikes_per_inference": null,
681
- "syn_ops_per_inference": 3271536.9975649347,
682
- "dense_macs_per_inference": 122675200.0,
683
- "event_fraction_of_dense": 0.026668283382174512,
684
- "syn_ops_per_inference_dense_fanout": 64848154.78409091,
685
- "weight_sparsity": 0.7324707846410685,
686
- "effective_ticks": 12.079285714285714,
687
- "num_ticks": 100,
688
- "membrane_bytes_per_inference": 451200,
689
- "weight_bytes": 4907008,
690
- "input_raster_bytes": null,
691
- "train_ms_per_step": 94.71398885000326,
692
- "infer_ms_per_sample": 0.6264105418749466,
693
- "peak_vram_mb": 2828.282368
694
- },
695
- "optimization_specific": {
696
- "calibrate_thresh": false,
697
- "ftbc": false,
698
- "calib_percentile": 99.9,
699
- "calib_batches": 8,
700
- "ftbc_target_ticks": 8,
701
- "ftbc_ref_ticks": 100,
702
- "trained_T": 100,
703
- "thresh_before": [
704
- 1.0,
705
- 1.0,
706
- 1.0,
707
- 1.0
708
  ],
709
- "thresh_after": null,
710
- "min_ticks_within_1pct_of_full": null,
711
- "min_ticks_within_1pct_per_arm": null,
712
- "speedup_at_min_ticks": null,
713
- "full_T_acc": null,
714
- "t_sweep_rows": null,
715
- "calibration_reference": "same network at full T (NOT a source ANN)"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
716
  },
717
  "config": {
718
  "method": "harmonic_matched",
@@ -752,296 +633,159 @@
752
  "decision_margin": 2.0,
753
  "export": false,
754
  "seed": 1234,
755
- "hub_dir": "/kaggle/working/hub/opt03_ftbc_threshold_balance/control_baseline",
756
  "repo_id": "UWU-R-13/SSVEP-SNN",
757
  "push_hf": false,
758
  "keep_epoch_weights": 1,
759
  "no_resume": true,
760
  "selftest": false,
761
- "baseline": true,
762
  "bench": true,
763
  "bench_warmup": 10,
764
  "bench_iters": 50,
765
  "bench_train_steps": 20,
766
- "calibrate_thresh": true,
767
- "calib_percentile": 99.9,
768
- "calib_batches": 8,
769
- "ftbc": true,
770
- "ftbc_target_ticks": 8,
771
- "ftbc_ref_ticks": 0,
772
- "ftbc_lr": 1.0,
773
  "t_sweep": "4,8,16,25,50,100",
774
  "sampling_rate": 250.0
775
  }
776
  },
777
  "zip": {
778
- "name": "control_baseline.zip",
779
- "files": 80,
780
- "bytes": 12244248,
781
- "MB": 12.2,
782
- "sha256": "c24f218a83b9e351cc6d43ef1b51d7b3f9e3bfa86c64ae22f40bc90ce4f087a4"
783
  }
784
  },
785
  {
786
- "key": "opt03_ftbc_threshold_balance/default",
787
- "tag": "default",
788
- "script": "opt03_ftbc_threshold_balance.py",
789
- "hf_folder": "applied/opt03_ftbc_threshold_balance",
790
- "phase": 1,
791
  "status": "ok",
792
- "duration_s": 372.1,
793
  "optimization_summary": {
794
- "optimization": "ftbc_threshold_balance",
795
- "research_section": "2. Temporal-Dimension Reduction (threshold balancing + FTBC)",
796
- "reference": "Rueckauer et al. arXiv:1612.04052; Sengupta et al. arXiv:1802.02627; FTBC: Forward Temporal Bias Correction, ECCV 2024",
797
- "script": "opt03_ftbc_threshold_balance.py",
798
  "baseline_mode": false,
799
  "method": "harmonic_matched",
800
  "accuracy": {
801
- "test_acc": 0.9292857142857143,
802
- "test_target_acc": 0.9292857142857143,
803
- "best_val_acc": 0.945
804
  },
805
  "efficiency": {
806
  "spikes_per_inference": null,
807
- "syn_ops_per_inference": 3270969.221590909,
808
  "dense_macs_per_inference": 122675200.0,
809
- "event_fraction_of_dense": 0.026663655095658365,
810
- "syn_ops_per_inference_dense_fanout": 64847587.008116886,
811
- "weight_sparsity": 0.7324707846410685,
812
- "effective_ticks": 12.059285714285714,
813
  "num_ticks": 100,
814
  "membrane_bytes_per_inference": 451200,
815
- "weight_bytes": 4907008,
816
  "input_raster_bytes": null,
817
- "train_ms_per_step": 92.7018213500105,
818
- "infer_ms_per_sample": 0.6145065031248009,
819
- "peak_vram_mb": 2825.316864
820
  },
821
  "optimization_specific": {
822
- "calibrate_thresh": true,
823
- "ftbc": true,
824
- "calib_percentile": 99.9,
825
- "calib_batches": 8,
826
- "ftbc_target_ticks": 8,
827
- "ftbc_ref_ticks": 100,
828
- "trained_T": 100,
829
- "thresh_before": [
830
- 1.0,
831
- 1.0,
832
- 1.0,
833
- 1.0
834
- ],
835
- "thresh_after": [
836
- 2.353323459625244,
837
- 0.8474444150924683,
838
- 1.1090540885925293,
839
- 2.6859006881713867
840
- ],
841
- "min_ticks_within_1pct_of_full": null,
842
- "min_ticks_within_1pct_per_arm": {
843
- "raw": 50,
844
- "thresh": 50,
845
- "ftbc": 100,
846
- "both": null
847
  },
848
- "speedup_at_min_ticks": null,
849
- "full_T_acc": 0.9442857142857143,
850
- "t_sweep_rows": [
851
  {
852
- "T": 4,
853
- "arm": "raw",
854
- "acc": 0.2621428571428571,
855
- "syn_per_inference": 1869022.726461039,
856
- "dense_per_inference": 4907008.0,
857
- "event_fraction_of_dense": 0.38088846124991826
 
858
  },
859
  {
860
- "T": 4,
861
- "arm": "thresh",
862
- "acc": 0.40214285714285714,
863
- "syn_per_inference": 1828094.6006493508,
864
- "dense_per_inference": 4907008.0,
865
- "event_fraction_of_dense": 0.37254771148719357
 
866
  },
867
  {
868
- "T": 4,
869
- "arm": "ftbc",
870
- "acc": 0.18285714285714286,
871
- "syn_per_inference": 1982641.3717532465,
872
- "dense_per_inference": 4907008.0,
873
- "event_fraction_of_dense": 0.4040428244162729
 
874
  },
875
  {
876
- "T": 4,
877
- "arm": "both",
878
- "acc": 0.27714285714285714,
879
- "syn_per_inference": 1953169.6939935065,
880
- "dense_per_inference": 4907008.0,
881
- "event_fraction_of_dense": 0.3980367861624653
882
- },
883
- {
884
- "T": 8,
885
- "arm": "raw",
886
- "acc": 0.5942857142857143,
887
- "syn_per_inference": 2050550.6517857143,
888
- "dense_per_inference": 9814016.0,
889
- "event_fraction_of_dense": 0.20894103410731288
890
- },
891
- {
892
- "T": 8,
893
- "arm": "thresh",
894
- "acc": 0.7785714285714286,
895
- "syn_per_inference": 1952516.9748376624,
896
- "dense_per_inference": 9814016.0,
897
- "event_fraction_of_dense": 0.19895188420700174
898
- },
899
- {
900
- "T": 8,
901
- "arm": "ftbc",
902
- "acc": 0.375,
903
- "syn_per_inference": 2353354.2873376627,
904
- "dense_per_inference": 9814016.0,
905
- "event_fraction_of_dense": 0.2397952364595353
906
- },
907
- {
908
- "T": 8,
909
- "arm": "both",
910
- "acc": 0.53,
911
- "syn_per_inference": 2284980.627435065,
912
- "dense_per_inference": 9814016.0,
913
- "event_fraction_of_dense": 0.23282829653375997
914
- },
915
- {
916
- "T": 16,
917
- "arm": "raw",
918
- "acc": 0.8028571428571428,
919
- "syn_per_inference": 2341053.414772727,
920
- "dense_per_inference": 19628032.0,
921
- "event_fraction_of_dense": 0.11927091899853878
922
- },
923
- {
924
- "T": 16,
925
- "arm": "thresh",
926
- "acc": 0.8985714285714286,
927
- "syn_per_inference": 2125678.341720779,
928
- "dense_per_inference": 19628032.0,
929
- "event_fraction_of_dense": 0.10829808825055813
930
- },
931
- {
932
- "T": 16,
933
- "arm": "ftbc",
934
- "acc": 0.645,
935
- "syn_per_inference": 2743122.541396104,
936
- "dense_per_inference": 19628032.0,
937
- "event_fraction_of_dense": 0.13975535302755282
938
- },
939
- {
940
- "T": 16,
941
- "arm": "both",
942
- "acc": 0.7207142857142858,
943
- "syn_per_inference": 2544346.234577922,
944
- "dense_per_inference": 19628032.0,
945
- "event_fraction_of_dense": 0.12962818863235612
946
- },
947
- {
948
- "T": 25,
949
- "arm": "raw",
950
- "acc": 0.8764285714285714,
951
- "syn_per_inference": 2563596.2670454546,
952
- "dense_per_inference": 30668800.0,
953
- "event_fraction_of_dense": 0.08358971551040323
954
- },
955
- {
956
- "T": 25,
957
- "arm": "thresh",
958
- "acc": 0.9285714285714286,
959
- "syn_per_inference": 2243644.229707792,
960
- "dense_per_inference": 30668800.0,
961
- "event_fraction_of_dense": 0.07315722264020086
962
- },
963
- {
964
- "T": 25,
965
- "arm": "ftbc",
966
- "acc": 0.8257142857142857,
967
- "syn_per_inference": 3023309.375,
968
- "dense_per_inference": 30668800.0,
969
- "event_fraction_of_dense": 0.09857931758008139
970
- },
971
- {
972
- "T": 25,
973
- "arm": "both",
974
- "acc": 0.81,
975
- "syn_per_inference": 2707661.5560064935,
976
- "dense_per_inference": 30668800.0,
977
- "event_fraction_of_dense": 0.08828716989274095
978
- },
979
- {
980
- "T": 50,
981
- "arm": "raw",
982
- "acc": 0.935,
983
- "syn_per_inference": 2919362.8060064935,
984
- "dense_per_inference": 61337600.0,
985
- "event_fraction_of_dense": 0.047594995663451024
986
- },
987
- {
988
- "T": 50,
989
- "arm": "thresh",
990
- "acc": 0.9371428571428572,
991
- "syn_per_inference": 2426220.323051948,
992
- "dense_per_inference": 61337600.0,
993
- "event_fraction_of_dense": 0.039555188384481105
994
- },
995
- {
996
- "T": 50,
997
- "arm": "ftbc",
998
- "acc": 0.9107142857142857,
999
- "syn_per_inference": 3453336.166396104,
1000
- "dense_per_inference": 61337600.0,
1001
- "event_fraction_of_dense": 0.05630047746237388
1002
- },
1003
- {
1004
- "T": 50,
1005
- "arm": "both",
1006
- "acc": 0.905,
1007
- "syn_per_inference": 2956368.487824675,
1008
- "dense_per_inference": 61337600.0,
1009
- "event_fraction_of_dense": 0.04819830720185784
1010
- },
1011
  {
1012
- "T": 100,
1013
- "arm": "raw",
1014
- "acc": 0.9442857142857143,
1015
- "syn_per_inference": 3270968.9375,
1016
- "dense_per_inference": 122675200.0,
1017
- "event_fraction_of_dense": 0.026663652779860967
1018
  },
1019
  {
1020
- "T": 100,
1021
- "arm": "thresh",
1022
- "acc": 0.9292857142857143,
1023
- "syn_per_inference": 2701710.5844155843,
1024
- "dense_per_inference": 122675200.0,
1025
- "event_fraction_of_dense": 0.02202328249243192
1026
  },
1027
  {
1028
- "T": 100,
1029
- "arm": "ftbc",
1030
- "acc": 0.9342857142857143,
1031
- "syn_per_inference": 3888789.481331169,
1032
- "dense_per_inference": 122675200.0,
1033
- "event_fraction_of_dense": 0.031699882953776876
1034
  },
1035
  {
1036
- "T": 100,
1037
- "arm": "both",
1038
- "acc": 0.9207142857142857,
1039
- "syn_per_inference": 3266400.9139610385,
1040
- "dense_per_inference": 122675200.0,
1041
- "event_fraction_of_dense": 0.02662641604791383
1042
  }
1043
  ],
1044
- "calibration_reference": "same network at full T (NOT a source ANN)"
 
 
1045
  },
1046
  "config": {
1047
  "method": "harmonic_matched",
@@ -1081,7 +825,7 @@
1081
  "decision_margin": 2.0,
1082
  "export": false,
1083
  "seed": 1234,
1084
- "hub_dir": "/kaggle/working/hub/opt03_ftbc_threshold_balance/default",
1085
  "repo_id": "UWU-R-13/SSVEP-SNN",
1086
  "push_hf": false,
1087
  "keep_epoch_weights": 1,
@@ -1092,63 +836,149 @@
1092
  "bench_warmup": 10,
1093
  "bench_iters": 50,
1094
  "bench_train_steps": 20,
1095
- "calibrate_thresh": true,
1096
- "calib_percentile": 99.9,
1097
- "calib_batches": 8,
1098
- "ftbc": true,
1099
- "ftbc_target_ticks": 8,
1100
- "ftbc_ref_ticks": 0,
1101
- "ftbc_lr": 1.0,
1102
- "t_sweep": "4,8,16,25,50,100",
1103
  "sampling_rate": 250.0
1104
  }
1105
  },
1106
  "zip": {
1107
- "name": "default.zip",
1108
  "files": 81,
1109
- "bytes": 12251189,
1110
- "MB": 12.3,
1111
- "sha256": "a2408411059da5b902543faccd650a7826414bf770223e315a19896579a36f61"
1112
  }
1113
  },
1114
  {
1115
- "key": "opt04_cudagraph_unroll/control_baseline",
1116
- "tag": "control_baseline",
1117
- "script": "opt04_cudagraph_unroll.py",
1118
- "hf_folder": "applied/opt04_cudagraph_unroll",
1119
- "phase": 1,
1120
  "status": "ok",
1121
- "duration_s": 320.6,
1122
  "optimization_summary": {
1123
- "optimization": "cudagraph_static_unroll",
1124
- "research_section": "2. Temporal-dimension reduction / 5. OS & runtime",
1125
- "reference": "CUDA Graphs static capture/replay applied to the unrolled T-tick event-driven inference loop",
1126
- "script": "opt04_cudagraph_unroll.py",
1127
- "baseline_mode": true,
1128
  "method": "harmonic_matched",
1129
  "accuracy": {
1130
- "test_acc": 0.9385714285714286,
1131
- "test_target_acc": 0.9385714285714286,
1132
- "best_val_acc": 0.9464285714285714
1133
  },
1134
  "efficiency": {
1135
  "spikes_per_inference": null,
1136
- "syn_ops_per_inference": 3254834.5267857146,
1137
  "dense_macs_per_inference": 122675200.0,
1138
- "event_fraction_of_dense": 0.02653213140704653,
1139
- "syn_ops_per_inference_dense_fanout": 64831452.31331169,
1140
- "weight_sparsity": 0.7324707846410685,
1141
- "effective_ticks": 12.097857142857142,
1142
  "num_ticks": 100,
1143
- "membrane_bytes_per_inference": 451200,
1144
- "weight_bytes": 4907008,
1145
  "input_raster_bytes": null,
1146
- "train_ms_per_step": 92.22967624664307,
1147
- "infer_ms_per_sample": 0.6272434443235397,
1148
- "peak_vram_mb": 2804.347904
1149
  },
1150
  "optimization_specific": {
1151
- "graph_enabled": false
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1152
  },
1153
  "config": {
1154
  "method": "harmonic_matched",
@@ -1188,124 +1018,160 @@
1188
  "decision_margin": 2.0,
1189
  "export": false,
1190
  "seed": 1234,
1191
- "hub_dir": "/kaggle/working/hub/opt04_cudagraph_unroll/control_baseline",
1192
  "repo_id": "UWU-R-13/SSVEP-SNN",
1193
  "push_hf": false,
1194
  "keep_epoch_weights": 1,
1195
  "no_resume": true,
1196
  "selftest": false,
1197
- "baseline": true,
1198
  "bench": true,
1199
  "bench_warmup": 10,
1200
  "bench_iters": 50,
1201
  "bench_train_steps": 20,
1202
- "event_capacity": 0,
1203
- "graph_warmup": 3,
1204
- "bench_batches": "1,8,32,128",
 
 
 
1205
  "sampling_rate": 250.0
1206
  }
1207
  },
1208
  "zip": {
1209
- "name": "control_baseline.zip",
1210
- "files": 80,
1211
- "bytes": 12254389,
1212
- "MB": 12.3,
1213
- "sha256": "90228451e4506fa57fd18dda54312748f259653c71dda8725406593029e0c194"
1214
  }
1215
  },
1216
  {
1217
- "key": "opt04_cudagraph_unroll/default",
1218
- "tag": "default",
1219
- "script": "opt04_cudagraph_unroll.py",
1220
- "hf_folder": "applied/opt04_cudagraph_unroll",
1221
- "phase": 1,
1222
  "status": "ok",
1223
- "duration_s": 501.8,
1224
  "optimization_summary": {
1225
- "optimization": "cudagraph_static_unroll",
1226
- "research_section": "2. Temporal-dimension reduction / 5. OS & runtime",
1227
- "reference": "CUDA Graphs static capture/replay applied to the unrolled T-tick event-driven inference loop",
1228
- "script": "opt04_cudagraph_unroll.py",
1229
  "baseline_mode": false,
1230
  "method": "harmonic_matched",
1231
  "accuracy": {
1232
- "test_acc": 0.9435714285714286,
1233
- "test_target_acc": 0.9435714285714286,
1234
- "best_val_acc": 0.95
1235
  },
1236
  "efficiency": {
1237
  "spikes_per_inference": null,
1238
- "syn_ops_per_inference": 3255047.327922078,
1239
  "dense_macs_per_inference": 122675200.0,
1240
- "event_fraction_of_dense": 0.026533866078246277,
1241
- "syn_ops_per_inference_dense_fanout": 64831665.11444805,
1242
- "weight_sparsity": 0.7324707846410685,
1243
- "effective_ticks": 11.80642857142857,
1244
  "num_ticks": 100,
1245
- "membrane_bytes_per_inference": 451200,
1246
- "weight_bytes": 4907008,
1247
  "input_raster_bytes": null,
1248
- "train_ms_per_step": 92.52521991729736,
1249
- "infer_ms_per_sample": 0.6286457926034927,
1250
- "peak_vram_mb": 2803.899904
1251
  },
1252
  "optimization_specific": {
1253
- "graph_enabled": true,
1254
- "event_capacity_scan": {
1255
- "cap_scan": 98901,
1256
- "batch_size_scan": 64,
1257
- "n_batches_scanned": 20,
1258
- "density_per_sample": 1545.328125,
1259
- "per_layer_caps": [
1260
- 98901,
1261
- 7501,
1262
- 3379,
1263
- 3291
1264
- ]
 
 
 
 
 
 
 
 
1265
  },
1266
- "capacity": 98901,
1267
- "overflow_ticks_frac": 0.0,
1268
- "graph_vs_eager_pred_agreement": 1.0,
1269
- "graph_capture_ms": 233.42132568359375,
1270
- "graphed_acc": 0.9435714285714286,
1271
- "kernel_launches_eager": 400,
1272
- "kernel_launches_graphed": 2,
1273
- "launch_reduction_x": 200.0,
1274
- "bench_batches": {
1275
- "1": {
1276
- "ms_per_sample_eager": 157.8002643585205,
1277
- "ms_per_sample_graphed": 32.571964263916016,
1278
- "throughput_eager_samples_per_s": 6.337124998270032,
1279
- "throughput_graphed_samples_per_s": 30.701249451750854,
1280
- "speedup_x": 4.844665279623167,
1281
- "capacity": 1546
 
 
1282
  },
1283
- "8": {
1284
- "ms_per_sample_eager": 20.341086983680725,
1285
- "ms_per_sample_graphed": 12.358421683311462,
1286
- "throughput_eager_samples_per_s": 49.161581227310094,
1287
- "throughput_graphed_samples_per_s": 80.91648153990228,
1288
- "speedup_x": 1.645929189416548,
1289
- "capacity": 12363
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1290
  },
1291
- "32": {
1292
- "ms_per_sample_eager": 5.914023816585541,
1293
- "ms_per_sample_graphed": 10.751050561666489,
1294
- "throughput_eager_samples_per_s": 169.08961326729144,
1295
- "throughput_graphed_samples_per_s": 93.01416584957377,
1296
- "speedup_x": 0.5500879921142168,
1297
- "capacity": 49451
1298
  },
1299
- "128": {
1300
- "ms_per_sample_eager": 1.9992999359965324,
1301
- "ms_per_sample_graphed": 9.524058550596237,
1302
- "throughput_eager_samples_per_s": 500.17507728351893,
1303
- "throughput_graphed_samples_per_s": 104.99725455146395,
1304
- "speedup_x": 0.20992100430455352,
1305
- "capacity": 197802
 
 
 
 
1306
  }
1307
- },
1308
- "speedup_x_at_batch1": 4.844665279623167
 
 
1309
  },
1310
  "config": {
1311
  "method": "harmonic_matched",
@@ -1345,7 +1211,7 @@
1345
  "decision_margin": 2.0,
1346
  "export": false,
1347
  "seed": 1234,
1348
- "hub_dir": "/kaggle/working/hub/opt04_cudagraph_unroll/default",
1349
  "repo_id": "UWU-R-13/SSVEP-SNN",
1350
  "push_hf": false,
1351
  "keep_epoch_weights": 1,
@@ -1356,18 +1222,21 @@
1356
  "bench_warmup": 10,
1357
  "bench_iters": 50,
1358
  "bench_train_steps": 20,
1359
- "event_capacity": 0,
1360
- "graph_warmup": 3,
1361
- "bench_batches": "1,8,32,128",
 
 
 
1362
  "sampling_rate": 250.0
1363
  }
1364
  },
1365
  "zip": {
1366
- "name": "default.zip",
1367
  "files": 81,
1368
- "bytes": 12253126,
1369
- "MB": 12.3,
1370
- "sha256": "dd13cd46e7240e145c6dd659b20ff6be55136f94e8e39058663d3ccbf1811fac"
1371
  }
1372
  }
1373
  ]
 
461
  }
462
  },
463
  {
464
+ "key": "opt07_sfa_integer_train/storm_reg_0",
465
+ "tag": "storm_reg_0",
466
+ "script": "opt07_sfa_integer_train.py",
467
+ "hf_folder": "applied/opt07_sfa_integer_train",
468
+ "phase": 5,
469
  "status": "ok",
470
+ "duration_s": 620.6,
471
  "optimization_summary": {
472
+ "optimization": "sfa_integer_train",
473
+ "research_section": "3. Training-Time Approximation for Fewer Timesteps (SFA) / spike-storm suppression",
474
+ "reference": "Yao et al., Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training, arXiv:2411.16061",
475
+ "script": "opt07_sfa_integer_train.py",
476
  "baseline_mode": false,
477
  "method": "harmonic_matched",
478
  "accuracy": {
479
+ "test_acc": 0.93,
480
+ "test_target_acc": 0.93,
481
+ "best_val_acc": 0.9335714285714286
482
  },
483
  "efficiency": {
484
  "spikes_per_inference": null,
485
+ "syn_ops_per_inference": 5285655.101461039,
486
+ "dense_macs_per_inference": 122675200.0,
487
+ "event_fraction_of_dense": 0.04308658230401123,
488
+ "syn_ops_per_inference_dense_fanout": 66862272.88798702,
489
  "weight_sparsity": 0.7324707846410685,
490
+ "effective_ticks": 4.083571428571428,
491
  "num_ticks": 100,
492
  "membrane_bytes_per_inference": 451200,
493
  "weight_bytes": 4907008,
494
  "input_raster_bytes": null,
495
+ "train_ms_per_step": 104.8419256499983,
496
+ "infer_ms_per_sample": 0.6984454099999482,
497
+ "peak_vram_mb": 2830.736384
 
498
  },
499
  "optimization_specific": {
500
+ "sfa_levels_D": 4,
501
+ "soft_reset": true,
502
+ "storm_reg": 0.0,
503
+ "trained_T": 100,
504
+ "infer_mode": "graded",
505
+ "min_ticks_within_1pct_of_full": 100,
506
+ "speedup_at_min_ticks": 1.0,
507
+ "full_T_acc": 0.93,
508
+ "t_sweep_rows": [
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
509
  {
510
+ "T": 4,
511
+ "acc": 0.12142857142857143,
512
+ "syn_per_inference": 2814512.5584415584,
513
+ "dense_per_inference": 4907008.0,
514
+ "infer_mode": "graded",
515
+ "ticks_speedup_vs_trained_T": 25.0
516
  },
517
  {
518
+ "T": 8,
519
+ "acc": 0.2307142857142857,
520
+ "syn_per_inference": 3556388.2735389615,
521
+ "dense_per_inference": 9814016.0,
522
+ "infer_mode": "graded",
523
+ "ticks_speedup_vs_trained_T": 12.5
524
  },
525
  {
526
+ "T": 16,
527
+ "acc": 0.5378571428571428,
528
+ "syn_per_inference": 4167199.856331169,
529
+ "dense_per_inference": 19628032.0,
530
+ "infer_mode": "graded",
531
+ "ticks_speedup_vs_trained_T": 6.25
532
  },
533
  {
534
+ "T": 25,
535
+ "acc": 0.7707142857142857,
536
+ "syn_per_inference": 4437607.62012987,
537
+ "dense_per_inference": 30668800.0,
538
+ "infer_mode": "graded",
539
+ "ticks_speedup_vs_trained_T": 4.0
540
  },
541
  {
542
+ "T": 50,
543
+ "acc": 0.8842857142857142,
544
+ "syn_per_inference": 4779261.4342532465,
545
+ "dense_per_inference": 61337600.0,
546
+ "infer_mode": "graded",
547
+ "ticks_speedup_vs_trained_T": 2.0
548
  },
549
  {
550
+ "T": 100,
551
+ "acc": 0.93,
552
+ "syn_per_inference": 5285655.101461039,
553
+ "dense_per_inference": 122675200.0,
554
+ "infer_mode": "graded",
555
+ "ticks_speedup_vs_trained_T": 1.0
556
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
557
  ],
558
+ "storm_ratio_peak_over_mean": 4.048530718036778,
559
+ "tick_activity_cv": 0.5872231855348422,
560
+ "mean_integer_activation": 0.10196488050743936,
561
+ "frac_activations_gt1": 0.02114660758525133,
562
+ "max_activation": 4.0,
563
+ "per_layer_activity": [
564
+ {
565
+ "layer": 0,
566
+ "mean_integer_activation": 0.18338739359751344,
567
+ "peak_tick_activation": 0.6446411237120628,
568
+ "storm_ratio": 3.515187773085857,
569
+ "tick_activity_cv": 0.5872231855348422,
570
+ "frac_activations_gt1": 0.02114660758525133,
571
+ "max_activation": 4.0
572
+ },
573
+ {
574
+ "layer": 1,
575
+ "mean_integer_activation": 0.04924224853515625,
576
+ "peak_tick_activation": 0.12945556640625,
577
+ "storm_ratio": 2.6289531907509436,
578
+ "tick_activity_cv": 0.3385586560128827,
579
+ "frac_activations_gt1": 0.0011788940100814216,
580
+ "max_activation": 4.0
581
+ },
582
+ {
583
+ "layer": 2,
584
+ "mean_integer_activation": 0.07326499938964844,
585
+ "peak_tick_activation": 0.2966156005859375,
586
+ "storm_ratio": 4.048530718036778,
587
+ "tick_activity_cv": 0.5675882158492334,
588
+ "frac_activations_gt1": 0.0014440917584579438,
589
+ "max_activation": 4.0
590
+ }
591
+ ],
592
+ "expanded_vs_graded_agreement": 1.0,
593
+ "expanded_acc": 0.8984375,
594
+ "graded_acc": 0.8984375,
595
+ "syn_ratio_expanded_over_graded": 1.000382822740147,
596
+ "expansion_caveat": "exact only at beta=1 (IAF); with beta<1 the expanded mode applies decay per sub-tick and drifts"
597
  },
598
  "config": {
599
  "method": "harmonic_matched",
 
633
  "decision_margin": 2.0,
634
  "export": false,
635
  "seed": 1234,
636
+ "hub_dir": "/kaggle/working/hub/opt07_sfa_integer_train/storm_reg_0",
637
  "repo_id": "UWU-R-13/SSVEP-SNN",
638
  "push_hf": false,
639
  "keep_epoch_weights": 1,
640
  "no_resume": true,
641
  "selftest": false,
642
+ "baseline": false,
643
  "bench": true,
644
  "bench_warmup": 10,
645
  "bench_iters": 50,
646
  "bench_train_steps": 20,
647
+ "sfa_levels": 4,
648
+ "soft_reset": true,
649
+ "storm_reg": 0.0,
650
+ "infer_mode": "graded",
 
 
 
651
  "t_sweep": "4,8,16,25,50,100",
652
  "sampling_rate": 250.0
653
  }
654
  },
655
  "zip": {
656
+ "name": "storm_reg_0.zip",
657
+ "files": 81,
658
+ "bytes": 12259837,
659
+ "MB": 12.3,
660
+ "sha256": "194904e94dac6ff6cfd7ba1ce7a3b3ffeb09cd84de849835a09aaac3268a4a08"
661
  }
662
  },
663
  {
664
+ "key": "opt08_mint_quant/u_bits_0",
665
+ "tag": "u_bits_0",
666
+ "script": "opt08_mint_quant.py",
667
+ "hf_folder": "applied/opt08_mint_quant",
668
+ "phase": 5,
669
  "status": "ok",
670
+ "duration_s": 341.5,
671
  "optimization_summary": {
672
+ "optimization": "mint_quant",
673
+ "research_section": "4. Membrane-Potential Memory-Wall Optimizations (multiplier-less integer quantization)",
674
+ "reference": "Yin et al., MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks, arXiv:2305.09850 (ASP-DAC 2024)",
675
+ "script": "opt08_mint_quant.py",
676
  "baseline_mode": false,
677
  "method": "harmonic_matched",
678
  "accuracy": {
679
+ "test_acc": 0.9407142857142857,
680
+ "test_target_acc": 0.9407142857142857,
681
+ "best_val_acc": 0.9421428571428572
682
  },
683
  "efficiency": {
684
  "spikes_per_inference": null,
685
+ "syn_ops_per_inference": 2152696.525466721,
686
  "dense_macs_per_inference": 122675200.0,
687
+ "event_fraction_of_dense": 0.0175479357316452,
688
+ "syn_ops_per_inference_dense_fanout": 64922516.865259744,
689
+ "weight_sparsity": 0.8335865765859767,
690
+ "effective_ticks": 12.575,
691
  "num_ticks": 100,
692
  "membrane_bytes_per_inference": 451200,
693
+ "weight_bytes": 613376,
694
  "input_raster_bytes": null,
695
+ "train_ms_per_step": 99.00966735000338,
696
+ "infer_ms_per_sample": 0.6622065121874954,
697
+ "peak_vram_mb": 2833.092608
698
  },
699
  "optimization_specific": {
700
+ "w_bits": 4,
701
+ "u_bits": 0,
702
+ "leak_shift": 0,
703
+ "beta_eff": 1.0,
704
+ "qat": true,
705
+ "batchnorm": true,
706
+ "membrane_memory_reduction_x": 1.0,
707
+ "weight_memory_reduction_x": 8.0,
708
+ "membrane_bytes_per_inference": 451200.0,
709
+ "working_set_bytes": 617888.0,
710
+ "baseline_working_set_bytes": 4911520.0,
711
+ "l2_cache_fits": true,
712
+ "baseline_l2_cache_fits": false,
713
+ "l2_budget_bytes": 2097152,
714
+ "membrane_saturation_frac": null,
715
+ "acc_vs_ubits": {
716
+ "2": 0.8964285714285715,
717
+ "4": 0.9521428571428572,
718
+ "8": 0.9521428571428572,
719
+ "0": 0.9407142857142857
 
 
 
 
 
720
  },
721
+ "bits_sweep_rows": [
 
 
722
  {
723
+ "u_bits": 2,
724
+ "acc": 0.8964285714285715,
725
+ "target_acc": 0.8964285714285715,
726
+ "membrane_bytes_per_neuron_per_tick": 0.25,
727
+ "membrane_memory_reduction_x": 16.0,
728
+ "membrane_saturation_frac": 0.09132064494680851,
729
+ "acc_drop_vs_fp32": 0.04428571428571426
730
  },
731
  {
732
+ "u_bits": 4,
733
+ "acc": 0.9521428571428572,
734
+ "target_acc": 0.9521428571428572,
735
+ "membrane_bytes_per_neuron_per_tick": 0.5,
736
+ "membrane_memory_reduction_x": 8.0,
737
+ "membrane_saturation_frac": 0.2783921348625886,
738
+ "acc_drop_vs_fp32": -0.011428571428571455
739
  },
740
  {
741
+ "u_bits": 8,
742
+ "acc": 0.9521428571428572,
743
+ "target_acc": 0.9521428571428572,
744
+ "membrane_bytes_per_neuron_per_tick": 1.0,
745
+ "membrane_memory_reduction_x": 4.0,
746
+ "membrane_saturation_frac": 0.32464220412234046,
747
+ "acc_drop_vs_fp32": -0.011428571428571455
748
  },
749
  {
750
+ "u_bits": 0,
751
+ "acc": 0.9407142857142857,
752
+ "target_acc": 0.9407142857142857,
753
+ "membrane_bytes_per_neuron_per_tick": 4.0,
754
+ "membrane_memory_reduction_x": 1.0,
755
+ "membrane_saturation_frac": null,
756
+ "acc_drop_vs_fp32": 0.0
757
+ }
758
+ ],
759
+ "fp32_membrane_acc": 0.9407142857142857,
760
+ "per_layer_scales": [
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
761
  {
762
+ "layer": 0,
763
+ "weight_scale": 0.034521830933434625,
764
+ "membrane_scale": 1.0,
765
+ "shared_scale_gauge_factor": 28.967177376200326
 
 
766
  },
767
  {
768
+ "layer": 1,
769
+ "weight_scale": 0.02071961760520935,
770
+ "membrane_scale": 1.0,
771
+ "shared_scale_gauge_factor": 48.263438981063956
 
 
772
  },
773
  {
774
+ "layer": 2,
775
+ "weight_scale": 0.013957016170024872,
776
+ "membrane_scale": 1.0,
777
+ "shared_scale_gauge_factor": 71.64855208433981
 
 
778
  },
779
  {
780
+ "layer": 3,
781
+ "weight_scale": 0.0242827194077628,
782
+ "membrane_scale": 1.0,
783
+ "shared_scale_gauge_factor": 41.18154903524997
 
 
784
  }
785
  ],
786
+ "shared_scale_note": "weight grid and membrane grid are the same grid up to a per-layer positive scalar that BatchNorm absorbs exactly; exact only with --batchnorm on",
787
+ "multiplier_less_compare": false,
788
+ "wall_clock_caveat": "this reference kernel computes in fp32 registers, so the traffic win does not appear in train_ms_per_step; a fixed-point core is where it becomes energy"
789
  },
790
  "config": {
791
  "method": "harmonic_matched",
 
825
  "decision_margin": 2.0,
826
  "export": false,
827
  "seed": 1234,
828
+ "hub_dir": "/kaggle/working/hub/opt08_mint_quant/u_bits_0",
829
  "repo_id": "UWU-R-13/SSVEP-SNN",
830
  "push_hf": false,
831
  "keep_epoch_weights": 1,
 
836
  "bench_warmup": 10,
837
  "bench_iters": 50,
838
  "bench_train_steps": 20,
839
+ "w_bits": 4,
840
+ "u_bits": 0,
841
+ "leak_shift": 0,
842
+ "qat": true,
843
+ "bits_sweep": "2,4,8,0",
844
+ "l2_bytes": 2097152,
 
 
845
  "sampling_rate": 250.0
846
  }
847
  },
848
  "zip": {
849
+ "name": "u_bits_0.zip",
850
  "files": 81,
851
+ "bytes": 12238321,
852
+ "MB": 12.2,
853
+ "sha256": "de8d0ec3134b59a7b9e26917542d60cf181033520d851a5638465f805487afa4"
854
  }
855
  },
856
  {
857
+ "key": "opt08_mint_quant/u_bits_3",
858
+ "tag": "u_bits_3",
859
+ "script": "opt08_mint_quant.py",
860
+ "hf_folder": "applied/opt08_mint_quant",
861
+ "phase": 5,
862
  "status": "ok",
863
+ "duration_s": 341.6,
864
  "optimization_summary": {
865
+ "optimization": "mint_quant",
866
+ "research_section": "4. Membrane-Potential Memory-Wall Optimizations (multiplier-less integer quantization)",
867
+ "reference": "Yin et al., MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks, arXiv:2305.09850 (ASP-DAC 2024)",
868
+ "script": "opt08_mint_quant.py",
869
+ "baseline_mode": false,
870
  "method": "harmonic_matched",
871
  "accuracy": {
872
+ "test_acc": 0.9578571428571429,
873
+ "test_target_acc": 0.9578571428571429,
874
+ "best_val_acc": 0.9628571428571429
875
  },
876
  "efficiency": {
877
  "spikes_per_inference": null,
878
+ "syn_ops_per_inference": 1648163.6732954546,
879
  "dense_macs_per_inference": 122675200.0,
880
+ "event_fraction_of_dense": 0.013435182280489085,
881
+ "syn_ops_per_inference_dense_fanout": 64593181.90016234,
882
+ "weight_sparsity": 0.8624946199394825,
883
+ "effective_ticks": 11.785714285714286,
884
  "num_ticks": 100,
885
+ "membrane_bytes_per_inference": 0,
886
+ "weight_bytes": 613376,
887
  "input_raster_bytes": null,
888
+ "train_ms_per_step": 98.38904980000507,
889
+ "infer_ms_per_sample": 0.6557833990625284,
890
+ "peak_vram_mb": 2840.912896
891
  },
892
  "optimization_specific": {
893
+ "w_bits": 4,
894
+ "u_bits": 3,
895
+ "leak_shift": 0,
896
+ "beta_eff": 1.0,
897
+ "qat": true,
898
+ "batchnorm": true,
899
+ "membrane_memory_reduction_x": 10.666666666666666,
900
+ "weight_memory_reduction_x": 8.0,
901
+ "membrane_bytes_per_inference": 42300.0,
902
+ "working_set_bytes": 613799.0,
903
+ "baseline_working_set_bytes": 4911520.0,
904
+ "l2_cache_fits": true,
905
+ "baseline_l2_cache_fits": false,
906
+ "l2_budget_bytes": 2097152,
907
+ "membrane_saturation_frac": 0.14972666638962767,
908
+ "acc_vs_ubits": {
909
+ "2": 0.5942857142857143,
910
+ "4": 0.9564285714285714,
911
+ "8": 0.955,
912
+ "0": 0.9528571428571428
913
+ },
914
+ "bits_sweep_rows": [
915
+ {
916
+ "u_bits": 2,
917
+ "acc": 0.5942857142857143,
918
+ "target_acc": 0.5942857142857143,
919
+ "membrane_bytes_per_neuron_per_tick": 0.25,
920
+ "membrane_memory_reduction_x": 16.0,
921
+ "membrane_saturation_frac": 0.019098722850177306,
922
+ "acc_drop_vs_fp32": 0.35857142857142854
923
+ },
924
+ {
925
+ "u_bits": 4,
926
+ "acc": 0.9564285714285714,
927
+ "target_acc": 0.9564285714285714,
928
+ "membrane_bytes_per_neuron_per_tick": 0.5,
929
+ "membrane_memory_reduction_x": 8.0,
930
+ "membrane_saturation_frac": 0.20831449468085106,
931
+ "acc_drop_vs_fp32": -0.0035714285714285587
932
+ },
933
+ {
934
+ "u_bits": 8,
935
+ "acc": 0.955,
936
+ "target_acc": 0.955,
937
+ "membrane_bytes_per_neuron_per_tick": 1.0,
938
+ "membrane_memory_reduction_x": 4.0,
939
+ "membrane_saturation_frac": 0.2674735427748227,
940
+ "acc_drop_vs_fp32": -0.002142857142857113
941
+ },
942
+ {
943
+ "u_bits": 0,
944
+ "acc": 0.9528571428571428,
945
+ "target_acc": 0.9528571428571428,
946
+ "membrane_bytes_per_neuron_per_tick": 4.0,
947
+ "membrane_memory_reduction_x": 1.0,
948
+ "membrane_saturation_frac": null,
949
+ "acc_drop_vs_fp32": 0.0
950
+ }
951
+ ],
952
+ "fp32_membrane_acc": 0.9528571428571428,
953
+ "per_layer_scales": [
954
+ {
955
+ "layer": 0,
956
+ "weight_scale": 0.03138868297849383,
957
+ "membrane_scale": 0.3333333333333333,
958
+ "shared_scale_gauge_factor": 10.619538690480226
959
+ },
960
+ {
961
+ "layer": 1,
962
+ "weight_scale": 0.019679610218320574,
963
+ "membrane_scale": 0.3333333333333333,
964
+ "shared_scale_gauge_factor": 16.93800485047307
965
+ },
966
+ {
967
+ "layer": 2,
968
+ "weight_scale": 0.009625924485070365,
969
+ "membrane_scale": 0.3333333333333333,
970
+ "shared_scale_gauge_factor": 34.62870853083538
971
+ },
972
+ {
973
+ "layer": 3,
974
+ "weight_scale": 0.015539673822266715,
975
+ "membrane_scale": 0.3333333333333333,
976
+ "shared_scale_gauge_factor": 21.45047168594374
977
+ }
978
+ ],
979
+ "shared_scale_note": "weight grid and membrane grid are the same grid up to a per-layer positive scalar that BatchNorm absorbs exactly; exact only with --batchnorm on",
980
+ "multiplier_less_compare": true,
981
+ "wall_clock_caveat": "this reference kernel computes in fp32 registers, so the traffic win does not appear in train_ms_per_step; a fixed-point core is where it becomes energy"
982
  },
983
  "config": {
984
  "method": "harmonic_matched",
 
1018
  "decision_margin": 2.0,
1019
  "export": false,
1020
  "seed": 1234,
1021
+ "hub_dir": "/kaggle/working/hub/opt08_mint_quant/u_bits_3",
1022
  "repo_id": "UWU-R-13/SSVEP-SNN",
1023
  "push_hf": false,
1024
  "keep_epoch_weights": 1,
1025
  "no_resume": true,
1026
  "selftest": false,
1027
+ "baseline": false,
1028
  "bench": true,
1029
  "bench_warmup": 10,
1030
  "bench_iters": 50,
1031
  "bench_train_steps": 20,
1032
+ "w_bits": 4,
1033
+ "u_bits": 3,
1034
+ "leak_shift": 0,
1035
+ "qat": true,
1036
+ "bits_sweep": "2,4,8,0",
1037
+ "l2_bytes": 2097152,
1038
  "sampling_rate": 250.0
1039
  }
1040
  },
1041
  "zip": {
1042
+ "name": "u_bits_3.zip",
1043
+ "files": 81,
1044
+ "bytes": 12230314,
1045
+ "MB": 12.2,
1046
+ "sha256": "59408685a190f6abc9f857e49ce34938251b9d14b3cc1bd8192fc8a52b9a9882"
1047
  }
1048
  },
1049
  {
1050
+ "key": "opt08_mint_quant/u_bits_4",
1051
+ "tag": "u_bits_4",
1052
+ "script": "opt08_mint_quant.py",
1053
+ "hf_folder": "applied/opt08_mint_quant",
1054
+ "phase": 5,
1055
  "status": "ok",
1056
+ "duration_s": 344.8,
1057
  "optimization_summary": {
1058
+ "optimization": "mint_quant",
1059
+ "research_section": "4. Membrane-Potential Memory-Wall Optimizations (multiplier-less integer quantization)",
1060
+ "reference": "Yin et al., MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks, arXiv:2305.09850 (ASP-DAC 2024)",
1061
+ "script": "opt08_mint_quant.py",
1062
  "baseline_mode": false,
1063
  "method": "harmonic_matched",
1064
  "accuracy": {
1065
+ "test_acc": 0.965,
1066
+ "test_target_acc": 0.965,
1067
+ "best_val_acc": 0.9628571428571429
1068
  },
1069
  "efficiency": {
1070
  "spikes_per_inference": null,
1071
+ "syn_ops_per_inference": 1883576.624797078,
1072
  "dense_macs_per_inference": 122675200.0,
1073
+ "event_fraction_of_dense": 0.015354176107290455,
1074
+ "syn_ops_per_inference_dense_fanout": 64875275.42613637,
1075
+ "weight_sparsity": 0.854853303683222,
1076
+ "effective_ticks": 12.19,
1077
  "num_ticks": 100,
1078
+ "membrane_bytes_per_inference": 0,
1079
+ "weight_bytes": 613376,
1080
  "input_raster_bytes": null,
1081
+ "train_ms_per_step": 99.37113355001657,
1082
+ "infer_ms_per_sample": 0.6619073024999977,
1083
+ "peak_vram_mb": 2841.84576
1084
  },
1085
  "optimization_specific": {
1086
+ "w_bits": 4,
1087
+ "u_bits": 4,
1088
+ "leak_shift": 0,
1089
+ "beta_eff": 1.0,
1090
+ "qat": true,
1091
+ "batchnorm": true,
1092
+ "membrane_memory_reduction_x": 8.0,
1093
+ "weight_memory_reduction_x": 8.0,
1094
+ "membrane_bytes_per_inference": 56400.0,
1095
+ "working_set_bytes": 613940.0,
1096
+ "baseline_working_set_bytes": 4911520.0,
1097
+ "l2_cache_fits": true,
1098
+ "baseline_l2_cache_fits": false,
1099
+ "l2_budget_bytes": 2097152,
1100
+ "membrane_saturation_frac": 0.2496060505319149,
1101
+ "acc_vs_ubits": {
1102
+ "2": 0.7471428571428571,
1103
+ "4": 0.965,
1104
+ "8": 0.9657142857142857,
1105
+ "0": 0.9592857142857143
1106
  },
1107
+ "bits_sweep_rows": [
1108
+ {
1109
+ "u_bits": 2,
1110
+ "acc": 0.7471428571428571,
1111
+ "target_acc": 0.7471428571428571,
1112
+ "membrane_bytes_per_neuron_per_tick": 0.25,
1113
+ "membrane_memory_reduction_x": 16.0,
1114
+ "membrane_saturation_frac": 0.032914381094858156,
1115
+ "acc_drop_vs_fp32": 0.2121428571428572
1116
+ },
1117
+ {
1118
+ "u_bits": 4,
1119
+ "acc": 0.965,
1120
+ "target_acc": 0.965,
1121
+ "membrane_bytes_per_neuron_per_tick": 0.5,
1122
+ "membrane_memory_reduction_x": 8.0,
1123
+ "membrane_saturation_frac": 0.2418986868351064,
1124
+ "acc_drop_vs_fp32": -0.005714285714285672
1125
  },
1126
+ {
1127
+ "u_bits": 8,
1128
+ "acc": 0.9657142857142857,
1129
+ "target_acc": 0.9657142857142857,
1130
+ "membrane_bytes_per_neuron_per_tick": 1.0,
1131
+ "membrane_memory_reduction_x": 4.0,
1132
+ "membrane_saturation_frac": 0.296513671875,
1133
+ "acc_drop_vs_fp32": -0.00642857142857145
1134
+ },
1135
+ {
1136
+ "u_bits": 0,
1137
+ "acc": 0.9592857142857143,
1138
+ "target_acc": 0.9592857142857143,
1139
+ "membrane_bytes_per_neuron_per_tick": 4.0,
1140
+ "membrane_memory_reduction_x": 1.0,
1141
+ "membrane_saturation_frac": null,
1142
+ "acc_drop_vs_fp32": 0.0
1143
+ }
1144
+ ],
1145
+ "fp32_membrane_acc": 0.9592857142857143,
1146
+ "per_layer_scales": [
1147
+ {
1148
+ "layer": 0,
1149
+ "weight_scale": 0.03545519496713366,
1150
+ "membrane_scale": 0.14285714285714285,
1151
+ "shared_scale_gauge_factor": 4.0292302154753035
1152
  },
1153
+ {
1154
+ "layer": 1,
1155
+ "weight_scale": 0.020786042724336897,
1156
+ "membrane_scale": 0.14285714285714285,
1157
+ "shared_scale_gauge_factor": 6.872743636280589
 
 
1158
  },
1159
+ {
1160
+ "layer": 2,
1161
+ "weight_scale": 0.010473860161645072,
1162
+ "membrane_scale": 0.14285714285714285,
1163
+ "shared_scale_gauge_factor": 13.639397571898177
1164
+ },
1165
+ {
1166
+ "layer": 3,
1167
+ "weight_scale": 0.017392768391541073,
1168
+ "membrane_scale": 0.14285714285714285,
1169
+ "shared_scale_gauge_factor": 8.213594273274003
1170
  }
1171
+ ],
1172
+ "shared_scale_note": "weight grid and membrane grid are the same grid up to a per-layer positive scalar that BatchNorm absorbs exactly; exact only with --batchnorm on",
1173
+ "multiplier_less_compare": true,
1174
+ "wall_clock_caveat": "this reference kernel computes in fp32 registers, so the traffic win does not appear in train_ms_per_step; a fixed-point core is where it becomes energy"
1175
  },
1176
  "config": {
1177
  "method": "harmonic_matched",
 
1211
  "decision_margin": 2.0,
1212
  "export": false,
1213
  "seed": 1234,
1214
+ "hub_dir": "/kaggle/working/hub/opt08_mint_quant/u_bits_4",
1215
  "repo_id": "UWU-R-13/SSVEP-SNN",
1216
  "push_hf": false,
1217
  "keep_epoch_weights": 1,
 
1222
  "bench_warmup": 10,
1223
  "bench_iters": 50,
1224
  "bench_train_steps": 20,
1225
+ "w_bits": 4,
1226
+ "u_bits": 4,
1227
+ "leak_shift": 0,
1228
+ "qat": true,
1229
+ "bits_sweep": "2,4,8,0",
1230
+ "l2_bytes": 2097152,
1231
  "sampling_rate": 250.0
1232
  }
1233
  },
1234
  "zip": {
1235
+ "name": "u_bits_4.zip",
1236
  "files": 81,
1237
+ "bytes": 12211406,
1238
+ "MB": 12.2,
1239
+ "sha256": "fa14fb4378b6e3737d3d49ed8bf58b569e33a530256e925d6c0ec6d07495a44d"
1240
  }
1241
  }
1242
  ]