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3D-integrated multilayered physical reservoir array for learning and forecasting time-series information
- Choi, Sanghyeon;
- Shin, Jaeho;
- Park, Gwanyeong;
- Eo, Jung Sun;
- Jang, Jingon;
- ... Wang, Gunuk;
- 외 1명
WEB OF SCIENCE
74SCOPUS
77초록
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. © The Author(s) 2024.
키워드
- 제목
- 3D-integrated multilayered physical reservoir array for learning and forecasting time-series information
- 저자
- Choi, Sanghyeon; Shin, Jaeho; Park, Gwanyeong; Eo, Jung Sun; Jang, Jingon; Yang, J. Joshua; Wang, Gunuk
- 발행일
- 2024-03
- 유형
- Article
- 권
- 15
- 호
- 1
- 언어
- ENG
- 출판사
- Nature Research
- 발행국가
- 영국
- ISSN
- E 2041-1723
P 2041-1723