Mixed‐Precision Continual Learning Based on Computational Resistance Random Access Memory-Reference-Cited by-同舟云学术

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Mixed‐Precision Continual Learning Based on Computational Resistance Random Access Memory

Published:2022-08 Issue:8 Volume:4 Page:2270036
ISSN:2640-4567
Container-title:Advanced Intelligent Systems
language:en
Short-container-title:Advanced Intelligent Systems

Author:

Li Yi¹²,Zhang Woyu¹²,Xu Xiaoxin¹,He Yifan³,Dong Danian¹,Jiang Nanjia¹,Wang Fei¹²,Guo Zeyu¹²,Wang Shaocong⁴,Dou Chunmeng¹,Liu Yongpan³,Wang Zhongrui⁴,Shang Dashan¹²^ORCID

Affiliation:

1. Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China

2. University of Chinese Academy of Sciences Beijing 101408 China

3. Department of Electronics Engineering Tsinghua University Beijing 100084 China

4. Department of Electrical and Electronic Engineering The University of Hong Kong Pok Fu Lam Road Hong Kong 999077 Hong Kong

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202270036

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A User-Friendly Fast and Accurate Simulation Framework for Non-Ideal Factors in Computing-in-Memory Architecture;2023 IEEE International Symposium on Circuits and Systems (ISCAS);2023-05-21

2. A survey and perspective on neuromorphic continual learning systems;Frontiers in Neuroscience;2023-05-04

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