Edge learning using a fully integrated neuro-inspired memristor chip

Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. W...

Full description

Saved in:
Bibliographic Details
Published in:Science (American Association for the Advancement of Science) Vol. 381; no. 6663; p. 1205
Main Authors: Zhang, Wenbin, Yao, Peng, Gao, Bin, Liu, Qi, Wu, Dong, Zhang, Qingtian, Li, Yuankun, Qin, Qi, Li, Jiaming, Zhu, Zhenhua, Cai, Yi, Wu, Dabin, Tang, Jianshi, Qian, He, Wang, Yu, Wu, Huaqiang
Format: Journal Article
Language:English
Published: 15.09.2023
ISSN:1095-9203, 1095-9203
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1095-9203
1095-9203
DOI:10.1126/science.ade3483