Machine learning at the mesoscale: a computation-dissipation bottleneck

07/05/2023
by   Alessandro Ingrosso, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset