Metrics for spin-based computing
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| author | Kurebayashi, Hidekazu Finocchio, Giovanni Everschor-Sitte, Karin Gartside, Jack C. Taniguchi, Tomohiro Litvinenko, Artem Kumar, Akash Åkerman, Johan Vasilaki, Eleni Selçuk, Kemal Çamsarı, Kerem Y. Madhavan, Advait Fukami, Shunsuke |
| author_facet | Kurebayashi, Hidekazu Finocchio, Giovanni Everschor-Sitte, Karin Gartside, Jack C. Taniguchi, Tomohiro Litvinenko, Artem Kumar, Akash Åkerman, Johan Vasilaki, Eleni Selçuk, Kemal Çamsarı, Kerem Y. Madhavan, Advait Fukami, Shunsuke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Metrics for spin-based computing Kurebayashi, Hidekazu Finocchio, Giovanni Everschor-Sitte, Karin Gartside, Jack C. Taniguchi, Tomohiro Litvinenko, Artem Kumar, Akash Åkerman, Johan Vasilaki, Eleni Selçuk, Kemal Çamsarı, Kerem Y. Madhavan, Advait Fukami, Shunsuke Mesoscale and Nanoscale Physics 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. |
| title | Metrics for spin-based computing |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2510.17653 |