Information Processing Capacity of Spintronic Oscillator

Tsunegi, Sumito; Kubota, Tomoyuki; Kamimaki, Akira; Grollier, Julie; Cros, Vincent; Yakushiji, Kay; Fukushima, Akio; Yuasa, Shinji; Kubota, Hitoshi; Nakajima, Kohei; Taniguchi, Tomohiro

doi:10.1002/aisy.202300175

Information Processing Capacity of Spintronic Oscillator

Journal Article · Fri Jun 30 00:00:00 EDT 2023 · Advanced Intelligent Systems

DOI:https://doi.org/10.1002/aisy.202300175· OSTI ID:1987936

Tsunegi, Sumito ^[1]; Kubota, Tomoyuki ^[2]; Kamimaki, Akira ^[3]; Grollier, Julie ^[4]; Cros, Vincent ^[4]; Yakushiji, Kay ^[3]; Fukushima, Akio ^[3]; Yuasa, Shinji ^[3]; Kubota, Hitoshi ^[3]; Nakajima, Kohei ^[2]; ^[3]

Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan, PRESTO Japan Science and Technology Agency (JST) Saitama 332-0012 Japan
Graduate School of Information Science and Technology The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan
Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan
Unité Mixte de Physique CNRS, Thales Université Paris-Saclay 91767 Palaiseau France

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.

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Research Organization:: Univ. of California, San Diego, CA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0019273

OSTI ID:: 1987936

Alternate ID(s):: OSTI ID: 2421080
OSTI ID: 1995337

Journal Information:: Advanced Intelligent Systems, Journal Name: Advanced Intelligent Systems Journal Issue: 9 Vol. 5; ISSN 2640-4567

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

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