In a Spin-Wave Reservoir for Machine Learning

Abstract

Reservoir computing is a computational framework which is originally based on software recurrent neural networks and recently achieved with physical systems as well. In our previous paper [Nakane et al., IEEE ACCESS vol. 6, p. 4462, 2018], we have proposed a spin-wave-based reservoir computing device with multiple input/output electrodes, and have demonstrated its high generalization ability in the estimation of input-signal parameters performed by the spin-wave-based reservoir computing. To successfully execute many types of estimation tasks with machine learning, it is necessary to investigate fundamental properties of spin-wave-based reservoir computing, particularly the relation between its input and output. From this background, the purposes of this work are to demonstrate a different estimation task with pulse input signals and to analyze the properties of spin waves which have important roles in the task. We first describe our approach to obtain spin waves with the features useful for reservoir computing, by considering the fundamental properties of spin waves and feasible device technologies. Then, we investigate detailed characteristics of locally-excited spin waves in a garnet film by micromagnetics simulation. Using the resultant spin waves, we demonstrate a pulse interval estimation task, and achieve a high diversity in the time-sequential signals generated by the spin-wave-based reservoir. The spin-wave-based device is a highly promising hardware for next-generation machine-learning electronics.