Machine learning at the mesoscale: a computation-dissipation bottleneck
Alessandro Ingrosso Emanuele Panizon
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.