Edge learning using a fully integrated neuro-inspired memristor chip

• 成果类型: Article
• 署名作者: 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
• 署名单位: Tsinghua University; Tsinghua University
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-9253
• DOI: 10.1126/science.ade3483
• 发表日期: 2023-09-15
• 页码: 1205-1211
• 关键词: in-memory; cmos; efficient

摘要

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.