Spintronic reservoir computing without driving current or magnetic field

TL;DR

This paper introduces a low-power spintronic reservoir computing method using voltage-controlled magnetization dynamics in nanostructured ferromagnets, demonstrating comparable computational performance to larger neural networks.

Contribution

It proposes a novel approach to reservoir computing that avoids current and magnetic field driving, utilizing voltage control for magnetization dynamics in a single MTJ.

Findings

Achieves benchmark task performance comparable to echo-state networks with over 10 nodes.

Demonstrates low-power potential of voltage-controlled magnetization dynamics.

Validates the approach through numerical simulations.

Abstract

Recent studies have shown that nonlinear magnetization dynamics excited in nanostructured ferromagnets are applicable to brain-inspired computing such as physical reservoir computing. The previous works have utilized the magnetization dynamics driven by electric current and/or magnetic field. This work proposes a method to apply the magnetization dynamics driven by voltage control of magnetic anisotropy to physical reservoir computing, which will be preferable from the viewpoint of low-power consumption. The computational capabilities of benchmark tasks in single MTJ are evaluated by numerical simulation of the magnetization dynamics and found to be comparable to those of echo-state networks with more than 10 nodes.