EDEN-ResNet50-Custom-ImageNet300

EDEN-ResNet50-Custom-ImageNet300 — EDEN Classic

Primary KPI: EAG (Energy-to-Accuracy Gradient) = N/A ΔAcc/ΔJoules

Abstract

This model is part of Project EDEN (Energy-Driven Evolution of Networks), implementing the E2AM (Energy Efficient Advanced Model) Framework. The goal is to shift AI benchmarking from pure accuracy to Green SOTA — maximising predictive power per Joule consumed.

Applied Technique: Phase 2 – EDEN Classic Energy-Aware Sparse Training

Profiling Environment

🟢 Green Delta Table

Comparing this model against the reference baseline (ResNet-50 equivalent)

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.

E2AM Algorithm — Applied Phases

Phase 1 – Zero-Overhead Initialization: Dataset cached in System RAM.

Phase 2 – EDEN Classic: Energy-aware training loop on classic CNN architectures. Applies the same EAG early-exit criterion (EAG < γ_EAG for 3 consecutive epochs → terminate), L1 sparsity penalty, and AMP to architectures like ResNet, VGG, AlexNet, DenseNet, InceptionV3, and UNet.

Training Statistics

📊 Training Visualizations

EAG Metric Trajectory

EAG = ΔAccuracy / ΔJoules — positive means learning more per Joule than baseline

Project-Wide Overview

All EDEN models: energy vs accuracy

Cite This Research

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