Fast physical reservoir computing, achieved with nonlinear interfered spin waves

Abstract

Abstract Reservoir computing is a promising approach to implementing high-performance artificial intelligence that can process input data at lower computational costs than conventional artificial neural networks. Although reservoir computing enables real-time processing of input time-series data on artificial intelligence mounted on terminal devices, few physical devices are capable of high-speed operation for real-time processing. In this study, we introduce spin wave interference with a stepped input method to reduce the operating time of the physical reservoir, and second-order nonlinear equation task and second-order nonlinear autoregressive mean averaging, which are well-known benchmark tasks, were carried out to evaluate the operating speed and prediction accuracy of said physical reservoir. The demonstrated reservoir device operates at the shortest operating time of 13 ms/5000-time steps, compared to other compact reservoir devices, even though its performance is higher than or comparable to such physical reservoirs. This study is a stepping stone toward realizing an artificial intelligence device capable of real-time processing on terminal devices.