MODEL_CARD.md

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	C5_cache_amp_gradaccum_adaptivelr_l1	0.5708	0.565335	739217	0.205338	9943.1	0.0975356	3518.37	20433688
cumulative_ablation	C3_cache_amp_gradaccum	0.4436	0.439476	724322	0.201201	9765.77	0.0955703	3453.76	20433688
cumulative_ablation	C2_cache_amp	0.3465	0.318729	712632	0.197953	9632.56	0.0940279	4147	20433688
cumulative_ablation	C1_cache	0.2502	0.19491	1.44514e+06	0.401428	19311.6	0.190678	4358.76	20433688
cumulative_ablation	C0_baseline	0.2475	0.211268	1.48291e+06	0.411919	19800.5	0.195662	4358.76	20433688
cumulative_ablation	C6_full_e2am	0.5708	0.565335	738944	0.205262	9939.1	0.0974996	3518.37	20433688
cumulative_ablation	C4_cache_amp_gradaccum_adaptivelr	0.5738	0.568648	729533	0.202648	9831.06	0.0962578	4228.98	20433688
individual_methods	M0_baseline_fp32	0.2462	0.223854	1.42266e+06	0.395184	18948.9	0.187712	4358.76	20433688
individual_methods	M4_l1_sparsity_only	0.244	0.210349	1.45956e+06	0.405434	19502.2	0.192581	4440.33	20433688
individual_methods	M3_grad_accum_only	0.3467	0.324298	1.41165e+06	0.392124	18791	0.186259	3383.49	20433688
individual_methods	M6_eag_only	0.2606	0.209208	1.4373e+06	0.39925	19318.7	0.189644	3285.48	20433688
individual_methods	M7_full_e2am	0.5805	0.575147	721653	0.200459	9682.67	0.0952181	3519.12	20433688
individual_methods	M1_cache_only	0.2451	0.182945	1.4199e+06	0.394418	19007.6	0.187348	4358.76	20433688
individual_methods	M5_adaptive_lr_only	0.4611	0.449641	1.4325e+06	0.397918	19208.2	0.189011	4358.76	20433688
individual_methods	M2_amp_only	0.3541	0.294041	697223	0.193673	9387.33	0.0919947	4229.98	20433688

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.