Reservoir Computing With Spin Waves Excited in a Garnet Film

TLDR

Reservoir computing is anticipated to enable energy‑efficient and high‑speed machine learning. The authors propose a reservoir computing device that uses spin waves propagating in a garnet film with multiple input/output electrodes. The device exploits the nonlinear interference of history‑dependent, asymmetrically propagating spin waves excited via the magneto‑electric effect, and its feasibility and generalization performance were demonstrated through micromagnetic simulations of forward volume magnetostatic spin waves. The spin‑wave reservoir achieves high diversity in time‑sequential signals, yielding superior generalization and promising performance for next‑generation machine‑learning electronics.

Abstract

We propose a reservoir computing device utilizing spin waves that propagate in a garnet film equipped with multiple input/output electrodes. In recent years, reservoir computing has been expected to realize energy-efficient and/or high-speed machine learning. Our proposed device enhances such significant merits in a hardware approach. It utilizes the nonlinear interference of history-dependent asymmetrically propagating spin waves excited by the magneto-electric effect. First, we investigate a feasible device structure with practical physical parameters in micromagnetic numerical analysis, and show the detailed characteristics of the forward volume magnetostatic spin waves. Then, we demonstrate high generalization ability in the estimation of input-signal parameters performed by the spin-wave-based reservoir computing. We find that the hysteresis characteristics of the spin waves propagating asymmetrically with respect to excitation points, as well as the nonlinear interference, works advantageously to realize high diversity in the time-sequential signals in high-dimensional information space, which has the highest significance for effective learning in reservoir computing. The spin wave device is highly promising for next-generation machine-learning electronics.