Passive frustrated nanomagnet reservoir computing

TL;DR

This paper introduces a passive nanomagnet reservoir computing system using frustrated nanomagnets, achieving high expressivity and resource efficiency suitable for low-power edge computing applications.

Contribution

It proposes a novel passive nanomagnet reservoir leveraging frustrated nanomagnets, fulfilling criteria for natural hardware reservoirs, with experimental validation and significant resource efficiency improvements.

Findings

Reservoir exhibits increased stable states due to frustration.

Experimental results confirm reservoir feasibility.

Resource consumption reduced by over 10 million times compared to CMOS echo-state networks.

Abstract

Reservoir computing (RC) has received recent interest because reservoir weights do not need to be trained, enabling extremely low-resource consumption implementations, which could have a transformative impact on edge computing and in-situ learning where resources are severely constrained. Ideally, a natural hardware reservoir should be passive, minimal, expressive, and feasible; to date, proposed hardware reservoirs have had difficulty meeting all of these criteria. We therefore propose a reservoir that meets all of these criteria by leveraging the passive interactions of dipole-coupled, frustrated nanomagnets. The frustration significantly increases the number of stable reservoir states, enriching reservoir dynamics, and as such these frustrated nanomagnets fulfill all of the criteria for a natural hardware reservoir. We likewise propose a complete frustrated nanomagnet reservoir computing (NMRC) system with low-power complementary metal-oxide semiconductor (CMOS) circuitry to interface with the reservoir, and initial experimental results demonstrate the reservoir's feasibility. The reservoir is verified with micromagnetic simulations on three separate tasks demonstrating expressivity. The proposed system is compared with a CMOS echo-state-network (ESN), demonstrating an overall resource decrease by a factor of over 10,000,000, demonstrating that because NMRC is naturally passive and minimal it has the potential to be extremely resource efficient.