Perspective on unconventional computing using magnetic skyrmions

Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field. (c) 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

Automated summary

Learning and pattern recognition require memory, which is simulated artificially in conventional CMOS hardware. Dynamical systems naturally provide the necessary memory, complexity, and nonlinearity for unconventional computing approaches. This article focuses on reservoir computing and provides an overview of key physical reservoir works, particularly in the promising platform of magnetic structures, such as skyrmions, for low-power applications. The article also discusses skyrmion-based implementations of Brownian computing, leveraging thermal fluctuations in skyrmion systems, and outlines the important challenges in this field.