Machine learning at the mesoscale: A computation-dissipation bottleneck

Phys Rev E. 2024 Jan;109(1-1):014132. doi: 10.1103/PhysRevE.109.014132.

Abstract

The cost of information processing in physical systems calls for a trade-off between performance and energetic expenditure. Here we formulate and study a computation-dissipation bottleneck in mesoscopic systems used as input-output devices. Using both real data sets and synthetic tasks, we show how nonequilibrium leads to enhanced performance. Our framework sheds light on a crucial compromise between information compression, input-output computation and dynamic irreversibility induced by nonreciprocal interactions.