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
Citations

WEB OF SCIENCE

74
Citations

SCOPUS

77

초록

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

MEMRISTOR; CLASSIFICATION; SYSTEM
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
DOI
10.1038/s41467-024-46323-7
발행일
2024-03
유형
Article
저널명
Nature Communications
권
15
호
1