Resist: Robust Network Training for Memristive Crossbar-Based Neuromorphic Computing Systems-Reference-Cited by-同舟云学术

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Resist: Robust Network Training for Memristive Crossbar-Based Neuromorphic Computing Systems

Published:2023-06 Issue:6 Volume:70 Page:2221-2225
ISSN:1549-7747
Container-title:IEEE Transactions on Circuits and Systems II: Express Briefs
language:
Short-container-title:IEEE Trans. Circuits Syst. II

Author:

Bi Yongtian¹,Xu Qi²^ORCID,Geng Hao³^ORCID,Chen Song²^ORCID,Kang Yi²^ORCID

Affiliation:

1. Institute of Advanced Technology, University of Science and Technology of China, Hefei, China

2. School of Microelectronics, University of Science and Technology of China, Hefei, China

3. School of Information Science and Technology, ShanghaiTech University, Shanghai, China

Funder

National Natural Science Foundation of China

CAS Project for Young Scientists in Basic Research

Strategic Priority Research Program of Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8920/10138208/10015093.pdf?arnumber=10015093

Reference20 articles.

1. Bayesian neural network enhancing reliability against conductance drift for memristor neural networks

2. Reliability-Driven Neural Network Training for Memristive Crossbar-Based Neuromorphic Computing Systems

3. RRAM Defect Modeling and Failure Analysis Based on March Test and a Novel Squeeze-Search Scheme

4. Dot-product engine for neuromorphic computing

5. Reliable and Robust RRAM-based Neuromorphic Computing

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