Potential implementation of Reservoir Computing models based on magnetic skyrmions
TL;DR: This paper argues that their nonlinear dynamical interplay resulting from anisotropic magnetoresistance and spin-torque effects allows for an effective and energy efficient nonlinear processing of spatial temporal events with the aim of event recognition.
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Abstract: Reservoir Computing is a type of recursive neural network commonly used for recognizing and predicting spatio-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The Reservoir Computing paradigm does not require any knowledge of the reservoir topology or node weights for training purposes and can therefore utilize naturally existing networks formed by a wide variety of physical processes. Most efforts prior to this have focused on utilizing memristor techniques to implement recursive neural networks. This paper examines the potential of skyrmion fabrics formed in magnets with broken inversion symmetry that may provide an attractive physical instantiation for Reservoir Computing.
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Citations
Recent advances in physical reservoir computing: A review
Gouhei Tanaka,Toshiyuki Yamane,Jean Benoit Héroux,Ryosho Nakane,Naoki Kanazawa,Seiji Takeda,Hidetoshi Numata,Daiju Nakano,Akira Hirose +8 more
TL;DR: An overview of recent advances in physical reservoir computing is provided by classifying them according to the type of the reservoir to expand its practical applications and develop next-generation machine learning systems.
Skyrmion-based artificial synapses for neuromorphic computing
Kyung Mee Song,Jaeseung Jeong,Biao Pan,Xichao Zhang,Jing Xia,Sun Kyung Cha,Tae Eon Park,Kwangsu Kim,Kwangsu Kim,Simone Finizio,Jörg Raabe,Joonyeon Chang,Joonyeon Chang,Yan Zhou,Weisheng Zhao,Wang Kang,Hyunsu Ju,Seonghoon Woo,Seonghoon Woo +18 more
- 01 Mar 2020
TL;DR: In this article, the accumulation and dissipation of magnetic skyrmions in ferrimagnetic multilayers can be controlled with electrical pulses to represent the variations in the synaptic weights.
472
Skyrmion-electronics: writing, deleting, reading and processing magnetic skyrmions toward spintronic applications.
Xichao Zhang,Yan Zhou,Kyung Mee Song,Tae Eon Park,Jing Xia,Motohiko Ezawa,Xiaoxi Liu,Weisheng Zhao,Guoping Zhao,Seonghoon Woo +9 more
TL;DR: The field of magnetic skyrmions has been actively investigated across a wide range of topics during the last decades as discussed by the authors, including information storage, logic computing gates and non-conventional devices such as neuromorphic computing devices.
454
The 2020 magnetism roadmap
E. Y. Vedmedenko,Roland Kawakami,Denis D. Sheka,Pietro Gambardella,Andrei Kirilyuk,Atsufumi Hirohata,Ch. Binek,Oksana Chubykalo-Fesenko,Stefano Sanvito,Brian J. Kirby,Julie Grollier,Karin Everschor-Sitte,Tobias Kampfrath,Tobias Kampfrath,Chun-Yeol You,Andreas Berger +15 more
TL;DR: The very relevant advances in the field of magnetism research during the past years make yet another Magnetism Roadmap a very sensible and timely endeavour, and allow its authors and readers to take another broad-based, but concise look at the most significant developments in magnetism.
The promise of spintronics for unconventional computing
Giovanni Finocchio,Massimiliano Di Ventra,Kerem Y. Camsari,Karin Everschor-Sitte,Pedram Khalili Amiri,Zhongming Zeng +5 more
TL;DR: In this article, the authors discuss how spintronics may aid in the realization of efficient devices, primarily focusing on magnetic tunnel junctions, and provide a perspective on how these devices can impact the development of three unconventional computing paradigms, namely, reservoir computing, probabilistic computing and memcomputing.
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