# 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 | C4_cache_amp_gradaccum_adaptivelr    |          0.3977 |         0.390035 |           370791 |           0.102998 |          4947.09 |      0.0489239 |        1662.97 |         28020296 |
| cumulative_ablation | C0_baseline                          |          0.3892 |         0.370385 |           748098 |           0.207805 |          9924.26 |      0.0987074 |        2867.09 |         28020296 |
| cumulative_ablation | C3_cache_amp_gradaccum               |          0.3772 |         0.355029 |           371606 |           0.103224 |          4950.59 |      0.0490313 |        1662.97 |         28020296 |
| cumulative_ablation | C1_cache                             |          0.3892 |         0.365102 |           756898 |           0.210249 |         10089    |      0.0998685 |        2861.06 |         28020296 |
| cumulative_ablation | C2_cache_amp                         |          0.3812 |         0.354907 |           388745 |           0.107985 |          5196.41 |      0.0512927 |        1545.73 |         28020296 |
| cumulative_ablation | C5_cache_amp_gradaccum_adaptivelr_l1 |          0.3919 |         0.385792 |           384648 |           0.106847 |          5140.54 |      0.0507521 |        1756.89 |         28020296 |
| cumulative_ablation | C6_full_e2am                         |          0.3919 |         0.385792 |           384772 |           0.106881 |          5132.64 |      0.0507685 |        1756.89 |         28020296 |
| individual_methods  | M1_cache_only                        |          0.3857 |         0.360327 |           777541 |           0.215984 |         10144.6  |      0.102592  |        1257.31 |         28020296 |
| individual_methods  | M7_full_e2am                         |          0.3962 |         0.38927  |           393985 |           0.10944  |          5169.17 |      0.0519841 |        1756.89 |         28020296 |
| individual_methods  | M3_grad_accum_only                   |          0.383  |         0.360399 |           750843 |           0.208568 |          9804.91 |      0.0990696 |        1339.67 |         28020296 |
| individual_methods  | M6_eag_only                          |          0.3906 |         0.357221 |           765768 |           0.212713 |         10251.6  |      0.101039  |        1256.06 |         28020296 |
| individual_methods  | M5_adaptive_lr_only                  |          0.4153 |         0.410143 |           777880 |           0.216078 |         10152.6  |      0.102637  |        2867.09 |         28020296 |
| individual_methods  | M0_baseline_fp32                     |          0.3906 |         0.360285 |           767680 |           0.213245 |         10299.6  |      0.101291  |        1256.06 |         28020296 |
| individual_methods  | M2_amp_only                          |          0.3828 |         0.36047  |           396535 |           0.110149 |          5223.2  |      0.0523206 |        1545.73 |         28020296 |
| individual_methods  | M4_l1_sparsity_only                  |          0.3915 |         0.3657   |           789122 |           0.219201 |         10569    |      0.10412   |        2862.59 |         28020296 |

## 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.