Abstract
The rapid growth of smart devices and sensors has led to an overwhelming increase in data generation, pushing current network infrastructure to its limits and threatening the scalability of cloud-based processing. Edge machine learning, which processes data locally on devices, presents a viable solution to reduce network load and latency. However, deploying deep learning at the edge remains difficult due to the limited memory and computational capacity of these devices which mostly precludes on-device/on-site training. Equilibrium propagation (EP) has emerged as a promising alternative to backpropagation, leveraging analog processing and device physics for energyefficient learning. Yet, its practical implementation is hindered by challenges such as voltage variations and the need for energyefficient circuits capable of gradient computation at a sufficient level of accuracy. Existing solutions rely on impractical idealized models. In this work, we introduce a novel method to address the problem of the wide dynamic range of the voltage variation to avoid the use of expensive low-noise amplifiers, and propose an innovative transistor-level switched-capacitor circuit to compute gradients in accordance with the EP rule. Additionally, our design supports batching, a key requirement for stable training that is often overlooked. We validate our approach on the MNIST dataset, demonstrating a practical, energy-efficient EP circuit that operates within real hardware constraints.