3D-integrated multilayered physical reservoir array for learning and forecasting time-series information

Sanghyeon Choi, Jaeho Shin, Gwanyeong Park, Jung Sun Eo, Jingon Jang, J. Joshua Yang () and Gunuk Wang ()
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Sanghyeon Choi: Korea University
Jaeho Shin: Korea University
Gwanyeong Park: Korea University
Jung Sun Eo: Korea University
Jingon Jang: Korea University
J. Joshua Yang: University of Southern California
Gunuk Wang: Korea University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.

Date: 2024
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DOI: 10.1038/s41467-024-46323-7

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