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Reservoir Computing Based on Spintronics Technology

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Reservoir Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

Recent developments in reservoir computing based on spintronics technology are described here. The rapid growth of brain-inspired computing has motivated researchers working in a broad range of scientific field to apply their own technologies, such as photonics, soft robotics, and quantum computing, to brain-inspired computing. A relatively new technology in condensed matter physics called spintronics is also a candidate for application to brain-inspired computing because the small size of devices (nanometer order), their low energy consumption, their rich magnetization dynamics, and so on are advantageous for realization of highly integrated network systems. In fact, several interesting functions, such as a spoken-digit recognition and an associative memory operation, achieved using spintronics technology have recently been demonstrated. Here, we describe our recent advances in the development of recurrent neural networks based on spintronics auto-oscillators, called spin-torque oscillators, such as experimental estimation of the short-term memory capacity of a vortex-type spin-torque oscillator and numerical simulation of reservoir computing using several macromagnetic oscillators. The results demonstrate the potential high performance of spintronics technology and its applicability to brain-inspired computing.

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Acknowledgements

The authors are thankful to Julie Grollier at CNRS/Thales for valuable discussions. S. T., T. T., and H. K. are grateful to Kay Yakushiji, Akio Fukushima, and Shinji Yuasa at the National Institute of Advanced Industrial Science and Technology (AIST) for their supports. S. M., K. F., S. T., and H. K. are grateful to Taishi Furuta, Eiiti Tamura, Minori Goto, Shota Hasebe, Koki Shimose, and Yoshishige Suzuki at the Osaka University for their discussions. T. T. is grateful to Takahide Kubota, Atsushi Sugihara, Satoshi Iba, Aurelie Spiesser, Hiroki Maehara, and Ai Emura for their support and encouragement. K. N., S. T., and T. T. are supported from a project subsidized by the New Energy and Industrial Technology Development Organization (NEDO). S. M. is supported by JSPS KAKENHI (No. JP18H03880, JP26103002). K. F. is supported by JSPS KAKENHI No. 16H02211, JST ERATO Grant No. JPMJER1601, JST CREST Grant No. JPMJCR1673, and JST PRESTO Grants No. JPMJPR15E7 and No. JPMJPR1668. K. N. is supported by JST PRESTO Grant Number JPMJPR15E7, Japan, and JSPS KAKENHI Grant Numbers JP18H05472, JP16KT0019, and JP15K16076.

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Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology (AIST), Research Center for Emerging Computing Technologies, Central 2, 1-1-1, Umezono, Tsukuba, Ibaraki, 305-8568, Japan

    Tomohiro Taniguchi, Sumito Tsunegi & Hitoshi Kubota

  2. The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Chiba, 277-8581, Japan

    Shinji Miwa

  3. Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531, Japan

    Keisuke Fujii

  4. Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo, Tokyo, 113-8656, Japan

    Kohei Nakajima

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  1. Tomohiro Taniguchi
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  2. Sumito Tsunegi
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  3. Shinji Miwa
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  4. Keisuke Fujii
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  5. Hitoshi Kubota
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  6. Kohei Nakajima
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Correspondence to Tomohiro Taniguchi .

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  1. University of Tokyo, Tokyo, Japan

    Kohei Nakajima

  2. Consejo Superior de Investigaciones Científicas, IFISC (UIB-CSIC), Palma, Spain

    Ingo Fischer

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Taniguchi, T., Tsunegi, S., Miwa, S., Fujii, K., Kubota, H., Nakajima, K. (2021). Reservoir Computing Based on Spintronics Technology. In: Nakajima, K., Fischer, I. (eds) Reservoir Computing. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-13-1687-6_14

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