Demonstration of Decentralized Physics-Driven Learning

Abstract

Leveraging physical processes rather than a central processor is key to building machine learning systems that are massively scalable, robust to damage, and energy-efficient, like the brain. To achieve these features, the authors build an electrical network made of identical resistive edges that self-adjust based on local conditions in order to minimize an energy-based global cost function when shown training examples. Problems like regression and data classification are successfully solved by this network. Due to their energy efficiency and scaling advantages, future versions may one day compete with computational neural networks.

Topics & Concepts