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

Cited 23 time in webofscience Cited 0 time in scopus
  • Hit : 217
  • Download : 0
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.
Publisher
AMER ASSOC ADVANCEMENT SCIENCE
Issue Date
2021-05
Language
English
Article Type
Article
Citation

SCIENCE ADVANCES, v.7, no.20

ISSN
2375-2548
DOI
10.1126/sciadv.abg1455
URI
http://hdl.handle.net/10203/285474
Appears in Collection
PH-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 23 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0