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<p>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, which is inherently non-volatile, nonlinear and fast operating, and can couple to other degrees of freedom such as photonic and phononic systems. This Technical Review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components such as radiofrequency neurons or synapses and spintronic probabilistic bits to broader frameworks such as reservoir computing and magnetic Ising machines. For each of these systems, we discuss hardware-specific and task-dependent metrics to evaluate their computing performance and evaluate the physical processes that need to be optimized to increase performance. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.</p>

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Metrics for spin-based computing

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Hidekazu Kurebayashi,
  • Giovanni Finocchio,
  • Karin Everschor-Sitte,
  • Jack C. Gartside,
  • Tomohiro Taniguchi,
  • Artem Litvinenko,
  • Akash Kumar,
  • Johan Åkerman,
  • Eleni Vasilaki,
  • Kemal Selçuk,
  • Kerem Y. Çamsarı,
  • Advait Madhavan,
  • Shunsuke Fukami

摘要

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, which is inherently non-volatile, nonlinear and fast operating, and can couple to other degrees of freedom such as photonic and phononic systems. This Technical Review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components such as radiofrequency neurons or synapses and spintronic probabilistic bits to broader frameworks such as reservoir computing and magnetic Ising machines. For each of these systems, we discuss hardware-specific and task-dependent metrics to evaluate their computing performance and evaluate the physical processes that need to be optimized to increase performance. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.