Task-adaptive physical reservoir computing.

ABSTRACT: Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in 'physical' reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a 'task-adaptive' approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu₂OSeO₃ that hosts skyrm

SUBMITTER: Lee O 

PROVIDER: S-EPMC10769874 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature