Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets

TL;DR

This paper introduces a frequency filtering approach in frustrated magnets to create thermally-robust spintronic reservoir computing systems capable of high-density, parallel, and nonlinear information processing.

Contribution

It proposes a novel frequency domain dynamics framework for spintronic reservoirs that enhances thermal robustness and enables frequency division multiplexing.

Findings

Demonstrates thermal noise resilience in spintronic reservoirs.

Shows feasibility of frequency division multiplexing in frustrated magnets.

Enables high-density spatiotemporal computation with single-spin units.

Abstract

Physical reservoir computing is a framework for brain-inspired information processing that utilizes nonlinear and high-dimensional dynamics in non-von-Neumann systems. In recent years, spintronic devices have been proposed for use as physical reservoirs, but their practical application remains a major challenge, mainly because thermal noise prevents them from retaining short-term memory, the essence of neuromorphic computing. Here, we propose a framework for spintronic physical reservoirs that exploits frequency domain dynamics in interacting spins. Through the effective use of frequency filters, we demonstrate, for a model of frustrated magnets, both robustness to thermal fluctuations and feasibility of frequency division multiplexing. This scheme can be coupled with parallelization in spatial domain even down to the level of a single spin, yielding a vast number of spatiotemporal computational units. Furthermore, the nonlinearity via the exchange interaction allows information processing among different frequency threads. Our findings establish a design principle for high-performance spintronic reservoirs with the potential for highly integrated devices.