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In-sensor reservoir computing for language learning via two-dimensional memristors

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Title
In-sensor reservoir computing for language learning via two-dimensional memristors
Author(s)
Sun, Linfeng; Wang, Zhongrui; Jinbao Jiange; Kim, Yeji; Joo, Bomin; Zheng, Shoujun; Lee, Seungyeon; Yu, Woo Jong; Kong, Bai-Sun; Yang, Heejun
Publication Date
2021-05
Journal
Science Advances, v.7, no.20
Publisher
American Association for the Advancement of Science
Abstract
© 2021 The Authors.The dynamic processing of optoelectronic signals carrying temporal and sequential information is critical to various machine learning applications including language processing and computer vision. Despite extensive efforts to emulate the visual cortex of human brain, large energy/time overhead and extra hardware costs are incurred by the physically separated sensing, memory, and processing units. The challenge is further intensified by the tedious training of conventional recurrent neural networks for edge deployment. Here, we report in-sensor reservoir computing for language learning. High dimensionality, nonlinearity, and fading memory for the in-sensor reservoir were achieved via two-dimensional memristors based on tin sulfide (SnS), uniquely having dual-type defect states associated with Sn and S vacancies. Our in-sensor reservoir computing demonstrates an accuracy of 91% to classify short sentences of language, thus shedding light on a low training cost and the real-time solution for processing temporal and sequential signals for machine learning applications at the edge.
URI
https://pr.ibs.re.kr/handle/8788114/10086
DOI
10.1126/sciadv.abg1455
ISSN
2375-2548
Appears in Collections:
Center for Integrated Nanostructure Physics(나노구조물리 연구단) > 1. Journal Papers (저널논문)
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