Tolerating Device-to-Device Variation for Memristive Crossbar-Based Neuromorphic Computing Systems: A New Bayesian Perspective-Reference-Cited by-同舟云学术

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Tolerating Device-to-Device Variation for Memristive Crossbar-Based Neuromorphic Computing Systems: A New Bayesian Perspective

Published:2023-06-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Joint Conference on Neural Networks (IJCNN)
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Author:

Xiao Yang¹,Xu Qi²,Yuan Bo³

Affiliation:

1. Research Institute of Trustworthy Autonomous System (RITAS), Southern University of Science and Technology (SUSTech),Shenzhen,China

2. School of Microelectronics, University of Science and Technology of China (USTC)

3. Southern University of Science and Technology (SUSTech),Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191448.pdf?arnumber=10191448

Reference22 articles.

1. Understanding memristive switching via in situ characterization and device modeling

2. Bayesian Inference Based Robust Computing on Memristor Crossbar

3. The missing memristor found

4. Memristor-The missing circuit element

5. Auto-encoding variational bayes;kingma;ArXiv Preprint,2013

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