Metrics for spin-based computing

TL;DR

This paper reviews recent advances in spin-based computing, emphasizing metrics for evaluating performance, integration into architectures, and future challenges in energy-efficient data processing technologies.

Contribution

It introduces hardware-specific and task-dependent metrics for assessing spintronic computing components and discusses their integration into various computational frameworks.

Findings

Metrics linking physical properties to computing performance

Integration of spintronic elements into neural and probabilistic architectures

Identification of challenges and future opportunities in spin-based computing

Abstract

Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by electrically manipulating the collective dynamics of the electron spin, that is inherently non-volatile, nonlinear and fast-operating, and can couple to other degrees of freedom such as photonic and phononic systems. This review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components like radio-frequency neurons/synapses and spintronic probabilistic-bits to broader frameworks such as reservoir computing and magnetic Ising machines. We discuss hardware-specific and task-dependent metrics to evaluate the computing performance of spin-based components and associate them with physical properties. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.