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.
doi:10.1063/5.0119040 (open access)
@article(Allwood++:2023,
author = "Dan A. Allwood and Matthew O. A. Ellis and David Griffin
and Thomas J. Hayward and Luca Manneschi and Mohammad F. Kh. Musameh
and Simon O'Keefe and Susan Stepney and Charles Swindells and Martin A. Trefzer
and Eleni Vasilaki and Guru Venkat and Ian Vidamour and Chester Wringe",
title = "A perspective on physical reservoir computing with nanomagnetic devices",
volume = "122",
pages = "040501",
year = 2023,
doi = "10.1063/5.0119040",
journal = "Applied Physics Letters"
)