Marrows, C.H. orcid.org/0000-0003-4812-6393, Barker, J., Moore, T.A. et al. (1 more author) (2024) Neuromorphic Computing with Spintronics. npj Spintronics, 2. 12. ISSN 2948-2119
Abstract
Spintronics and magnetic materials exhibit many physical phenomena that are promising for implementing neuromorphic computing natively in hardware. Here, we review the current state-of-the-art, focusing on the areas of spintronic synapses, neurons, and neural networks. Many current implementations are based on the paradigm of reservoir computing, where the details of the network do not need to be known but where significant post-processing is needed. Benchmarks are given where possible. We discuss the scientific and technological advances needed to bring about spintronic neuromorphic computing that could be useful to an end-user in the medium term.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Physics and Astronomy (Leeds) > Condensed Matter (Leeds) |
Funding Information: | Funder Grant number QinetiQ Limited QinetiQ Processing Centre 2022-CORP-NDA-013551 |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Mar 2024 13:47 |
Last Modified: | 02 May 2024 16:42 |
Status: | Published |
Publisher: | Nature Research |
Identification Number: | 10.1038/s44306-024-00019-2 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209808 |