Allwood, D.A., Ellis, M.O.A. orcid.org/0000-0003-0338-8920, Griffin, D. orcid.org/0000-0002-4077-0005 et al. (11 more authors) (2023) A perspective on physical reservoir computing with nanomagnetic devices. Applied Physics Letters, 122 (4). 040501. ISSN 0003-6951
Abstract
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here, we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.
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Copyright, Publisher and Additional Information: | © 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | ||||||||
Keywords: | Engineering; Physical Sciences; Affordable and Clean Energy | ||||||||
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Institution: | The University of Sheffield | ||||||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) |
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Depositing User: | Symplectic Sheffield | ||||||||
Date Deposited: | 24 Jan 2024 15:45 | ||||||||
Last Modified: | 24 Jan 2024 15:45 | ||||||||
Status: | Published | ||||||||
Publisher: | AIP Publishing | ||||||||
Refereed: | Yes | ||||||||
Identification Number: | https://doi.org/10.1063/5.0119040 | ||||||||
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