arXiv AI
Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Hardware-Validated Benchmarking Study of Forward-Only Algorithms
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PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation
The paper introduces PotentialEP, a backpropagation‑free method for training generative models at CIFAR scale using Equilibrium Propagation. It shows that a predictive coding network with a small linear injection can relax to a state that inherently carries the gradient needed for sampling, allowing an Energy Matching model trained with backpropagation to be sampled by relaxation alone with negligible loss in FID. Moreover, the authors demonstrate that training end‑to‑end with Equilibrium Propagation yields FID scores comparable to backpropagation, marking the first competitive CIFAR‑10 generative model trained without backpropagation.