Machine learning at the mesoscale: a computation-dissipation bottleneck

• URL: http://arxiv.org/abs/2307.02379v1
• Date: Wed, 5 Jul 2023 15:46:07 GMT
• Title: Machine learning at the mesoscale: a computation-dissipation bottleneck
• Authors: Alessandro Ingrosso and Emanuele Panizon
• Abstract summary: We study a computation-dissipation bottleneck in mesoscopic systems used as input-output devices. Our framework sheds light on a crucial compromise between information compression, input-output computation and dynamic irreversibility induced by non-reciprocal interactions.
• Score: 77.34726150561087
• License: http://creativecommons.org/licenses/by/4.0/
• 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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