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Primary KPI: EAG (Energy-to-Accuracy Gradient) =N/AΔAcc/ΔJoules
| Component | Specification |
|---|---|
| GPU | NVIDIA GeForce GTX 1080 Ti (11 GB VRAM, 250 W TDP) |
| CPU | Intel Xeon W-2125 (4 cores / 8 threads @ 4.00 GHz) |
| RAM | 63.66 GB System RAM |
| OS | Windows 10 |
| Dataset | CIFAR-10 — 60,000 images – 10 classes (32×32 px) |
| Metric | ResNet50 Baseline | UNet (EDEN) | Δ |
|---|---|---|---|
| Accuracy | 0.9354 | 0.9951 | N/A |
| Total Energy (J) | 40,291,739 | 0 | N/A saved |
| CO₂ Emissions (kg) | 5.3163 | 0.0000 | — |
| EAG Score | — | N/A | ΔAcc/ΔJoules |
A positive EAG means this model learns more per Joule than the baseline. A negative EAG indicates a trade-off where higher accuracy required more energy investment.
EAG < γ_EAG for 3 consecutive epochs → terminate), L1 sparsity penalty, and AMP to architectures like ResNet, VGG, AlexNet, DenseNet, InceptionV3, and UNet.| Metric | Value |
|---|---|
| Final Accuracy | 0.9951 (99.51%) |
| Total Energy Consumed | 0 J (0.0000 kWh) |
| Training Time | 0 s (0.00 hrs) |
| Estimated CO₂ | 0.0000 kg CO₂e |
| Training Log | test3\unet_classifier_EDEN_CIFAR10_stats.csv |
EAG = ΔAccuracy / ΔJoules — positive means learning more per Joule than baseline


1@misc{eden2025,
2 title = {Project EDEN: Energy-Driven Evolution of Networks},
3 author = {EDEN Research Team},
4 year = {2025},
5 note = {Hugging Face: Shanmuk4622},
6 url = {https://huggingface.co/Shanmuk4622}
7}