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Allwood, Dan A.; Ellis, Matthew O. A.; Griffin, David; Hayward, Thomas J.; Manneschi, Luca; Musameh, Mohammad F. KH.; O'Keefe, Simon; Stepney, Susan; Swindells, Charles; Trefzer, Martin A.; Vasilaki, Eleni; Venkat, Guru; Vidamour, Ian; Wringe, Chester

A perspective on physical reservoir computing with nanomagnetic devices

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  • Media type: E-Article
  • Title: A perspective on physical reservoir computing with nanomagnetic devices
  • Contributor: Allwood, Dan A.; Ellis, Matthew O. A.; Griffin, David; Hayward, Thomas J.; Manneschi, Luca; Musameh, Mohammad F. KH.; O'Keefe, Simon; Stepney, Susan; Swindells, Charles; Trefzer, Martin A.; Vasilaki, Eleni; Venkat, Guru; Vidamour, Ian; Wringe, Chester
  • Published: AIP Publishing, 2023
  • Published in: Applied Physics Letters, 122 (2023) 4
  • Language: English
  • DOI: 10.1063/5.0119040
  • ISSN: 0003-6951; 1077-3118
  • Origination:
  • Footnote:
  • Description: 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.