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E2AM_EfficientNetV2_S

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E2AM_EfficientNetV2_S / MODEL_CARD.md
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E2AM-ResNet50 — Model Card

Energy-aware ResNet-50 from scratch on CIFAR-10.

Architecture

  • ResNet-50 (torchvision.models.resnet50(weights=None))
  • CIFAR adaptation: conv1 -> 3x3 stride-1, maxpool -> Identity
  • Input: 32x32 RGB, CIFAR-10 normalization stats
  • Trained from scratch (no pretrained weights, no transfer)

Training environment

  • Platform: Kaggle Dual-T4 notebook (single T4 used for training)
  • Energy: nvidia-smi power.draw at 1 Hz, trapezoidal integration
  • Carbon: CO2_kg = (E_J / 3.6e6) * intensity

Results

method_group variant_name best_accuracy final_f1_score total_energy_j total_energy_kwh total_time_sec total_co2_kg peak_vram_mb num_parameters
cumulative_ablation C1_cache 0.7472 0.715194 714489 0.198469 9556 0.0942729 4356.43 20190298
cumulative_ablation C0_baseline 0.7569 0.756763 715745 0.198818 9591.36 0.0944386 4275.37 20190298
cumulative_ablation C2_cache_amp 0.8068 0.780503 355666 0.0987962 4823.6 0.0469282 4141.09 20190298
cumulative_ablation C6_full_e2am 0.904 0.90076 362560 0.100711 4914.65 0.0478378 3517.29 20190298
cumulative_ablation C5_cache_amp_gradaccum_adaptivelr_l1 0.904 0.90076 362232 0.10062 4909.43 0.0477944 3517.29 20190298
cumulative_ablation C3_cache_amp_gradaccum 0.8345 0.82655 355558 0.0987662 4810.3 0.0469139 3451.18 20190298
cumulative_ablation C4_cache_amp_gradaccum_adaptivelr 0.8973 0.896878 356142 0.0989283 4825.42 0.0469909 3451.18 20190298
individual_methods M7_full_e2am 0.8974 0.897066 361860 0.100517 4846.89 0.0477454 3517.29 20190298
individual_methods M0_baseline_fp32 0.7565 0.710483 748874 0.20802 9714.07 0.0988097 3274.15 20190298
individual_methods M2_amp_only 0.8013 0.782524 355111 0.0986419 4774.62 0.0468549 4141.09 20190298
individual_methods M3_grad_accum_only 0.8036 0.80465 703651 0.195459 9402.75 0.0928428 3377.66 20190298
individual_methods M6_eag_only 0.759 0.748992 748945 0.20804 9713.19 0.0988191 3274.15 20190298
individual_methods M1_cache_only 0.7582 0.721166 706610 0.19628 9440.23 0.0932332 3279.65 20190298
individual_methods M5_adaptive_lr_only 0.8841 0.883535 706780 0.196328 9453.78 0.0932557 4275.37 20190298
individual_methods M4_l1_sparsity_only 0.7394 0.711569 755073 0.209743 9834.08 0.0996277 4436.5 20190298

Limitations

  • Energy is integrated from per-GPU nvidia-smi power samples; CPU energy is not measured. Numbers reflect GPU-only training energy.
  • Pruning (D1/D2) is mask-based structured pruning — weights are zeroed but the dense layout is preserved. Real wall-clock speed-ups need a sparsity- aware runtime; reported numbers reflect masked weights.
  • INT8 (D3/D4) uses CPU FX static quantization (fbgemm); GPU INT8 via TensorRT is out of scope.
  • ResNet-50 with the small-image stem (conv1 3x3 stride-1, maxpool=Identity). Used for CIFAR-10 (32x32) and Tiny-ImageNet-200 (64x64). For ImageNet (224x224) the standard 7x7 stride-2 conv1 + maxpool would be needed instead.
  • Single T4 by design — DataParallel was measured slower at this scale.