EDEN-InceptionV3-CIFAR-10 — Baseline – Standard Full Training (Reference Study)

Primary KPI: EAG (Energy-to-Accuracy Gradient) — see Green Delta Table below.

Abstract

This model is part of Project EDEN (Energy-Driven Evolution of Networks). It serves as the Brute-Force Baseline for the InceptionV3 architecture on CIFAR-10, providing a transparent energy reference for EAG benchmarking against EDEN-optimized models.

Applied Technique: Baseline – Standard Full Training (Reference Study)

Profiling Environment

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
Dataset	CIFAR-10 — 60,000 images – 10 classes (32×32 px)

🟢 Green Delta Table

This is the reference baseline. Compare against EDEN-optimized models for EAG.

Metric	InceptionV3 Baseline	EDEN Optimized	Δ
Accuracy	See CSV log	See SOTA repo	—
Total Energy (J)	See CSV log	See SOTA repo	—
EAG Score	—	See SOTA repo	ΔAcc/ΔJoules

E2AM Algorithm — Applied Phase

Standard full fine-tuning used as the Brute-Force Baseline for energy comparison. All layers trained from epoch 1 with a fixed learning rate and no gradient accumulation. Included for transparent EAG benchmarking.

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}
}