Machine learning at the mesoscale: a computation-dissipation bottleneck

Alessandro Ingrosso; Emanuele Panizon

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arXiv · 2307.02379

Machine learning at the mesoscale: a computation-dissipation bottleneck

Alessandro Ingrosso·Emanuele Panizon

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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 datasets and synthetic tasks, we show how non-equilibrium leads to enhanced performance. Our framework sheds light on a crucial compromise between information compression, input-output computation and dynamic irreversibility induced by non-reciprocal interactions.

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cond-mat.stat-mech cond-mat.dis-nn cs.LG

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Alessandro Ingrosso, Emanuele Panizon. 2023-07-05. Machine learning at the mesoscale: a computation-dissipation bottleneck. https://arxiv.org/abs/2307.02379

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