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Perspective on unconventional computing using magnetic skyrmions

Oscar Lee, Robin Msiska (UGent) , Maarten A. Brems, Mathias Kläui, Hidekazu Kurebayashi and Karin Everschor-Sitte
(2023) APPLIED PHYSICS LETTERS. 122(26).
Author
Organization
Abstract
Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field.
Keywords
STATE, TEMPERATURE, COMPUTATION, CHAOS, EDGE, NETWORKS, CIRCUITS, LATTICE, ENERGY

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Citation

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MLA
Lee, Oscar, et al. “Perspective on Unconventional Computing Using Magnetic Skyrmions.” APPLIED PHYSICS LETTERS, vol. 122, no. 26, 2023, doi:10.1063/5.0148469.
APA
Lee, O., Msiska, R., Brems, M. A., Kläui, M., Kurebayashi, H., & Everschor-Sitte, K. (2023). Perspective on unconventional computing using magnetic skyrmions. APPLIED PHYSICS LETTERS, 122(26). https://doi.org/10.1063/5.0148469
Chicago author-date
Lee, Oscar, Robin Msiska, Maarten A. Brems, Mathias Kläui, Hidekazu Kurebayashi, and Karin Everschor-Sitte. 2023. “Perspective on Unconventional Computing Using Magnetic Skyrmions.” APPLIED PHYSICS LETTERS 122 (26). https://doi.org/10.1063/5.0148469.
Chicago author-date (all authors)
Lee, Oscar, Robin Msiska, Maarten A. Brems, Mathias Kläui, Hidekazu Kurebayashi, and Karin Everschor-Sitte. 2023. “Perspective on Unconventional Computing Using Magnetic Skyrmions.” APPLIED PHYSICS LETTERS 122 (26). doi:10.1063/5.0148469.
Vancouver
1.
Lee O, Msiska R, Brems MA, Kläui M, Kurebayashi H, Everschor-Sitte K. Perspective on unconventional computing using magnetic skyrmions. APPLIED PHYSICS LETTERS. 2023;122(26).
IEEE
[1]
O. Lee, R. Msiska, M. A. Brems, M. Kläui, H. Kurebayashi, and K. Everschor-Sitte, “Perspective on unconventional computing using magnetic skyrmions,” APPLIED PHYSICS LETTERS, vol. 122, no. 26, 2023.
@article{01JE4XNBYSVD5P5BESP5Z6T1P5,
  abstract     = {{Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field.}},
  articleno    = {{260501}},
  author       = {{Lee, Oscar and Msiska, Robin and Brems, Maarten A. and Kläui, Mathias and Kurebayashi, Hidekazu and Everschor-Sitte, Karin}},
  issn         = {{0003-6951}},
  journal      = {{APPLIED PHYSICS LETTERS}},
  keywords     = {{STATE,TEMPERATURE,COMPUTATION,CHAOS,EDGE,NETWORKS,CIRCUITS,LATTICE,ENERGY}},
  language     = {{eng}},
  number       = {{26}},
  pages        = {{13}},
  title        = {{Perspective on unconventional computing using magnetic skyrmions}},
  url          = {{http://doi.org/10.1063/5.0148469}},
  volume       = {{122}},
  year         = {{2023}},
}
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