Universal scaling between wave speed and size enables nanoscale high-performance reservoir computing based on propagating spin-waves

TL;DR

This paper demonstrates that the scaling relationship between wave speed and size enables high-performance nanoscale reservoir computing using propagating spin-waves, combining simulations and theory to achieve miniaturization without sacrificing computational power.

Contribution

It reveals a universal scaling law linking wave speed and system size, enabling high-performance nanoscale neuromorphic computing with spin waves.

Findings

Spin-wave reservoir computing can be miniaturized to nanoscales.

High computational power is maintained at nanoscales.

System size scaling with wave speed is crucial for performance.

Abstract

Neuromorphic computing using spin waves is promising for high-speed nanoscale devices, but the realization of high performance has not yet been achieved. Here we show, using micromagnetic simulations and simplified theory with response functions, that spin-wave physical reservoir computing can achieve miniaturization down to nanoscales keeping high computational power comparable with other state-of-art systems. We also show the scaling of system sizes with the propagation speed of spin waves plays a key role to achieve high performance at nanoscales.