Study on Memristor-Based 3-D Convolutional Neural Network Adapting to Non-Ideal Effects-Reference-Cited by-同舟云学术

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Study on Memristor-Based 3-D Convolutional Neural Network Adapting to Non-Ideal Effects

Published:2023-03 Issue:3 Volume:70 Page:993-1000
ISSN:0018-9383
Container-title:IEEE Transactions on Electron Devices
language:
Short-container-title:IEEE Trans. Electron Devices

Author:

Dong Boyi¹,Li Peixuan²,Fu Yaoyao¹,Chang Ting-Chang¹^ORCID,Li Yi³^ORCID,Miao Xiang-Shui¹^ORCID,He Yuhui¹^ORCID

Affiliation:

1. Hubei Yangtze Memory Laboratories, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China

2. School of Reliability and System Engineering, Beihang University, Beijing, China

3. Department of Physics, National Sun Yat-sen University, Kaohsiung, Taiwan

Funder

National Key Research and Development Plan of MOST of China

Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/16/10053598/10011182.pdf?arnumber=10011182

Reference39 articles.

1. Enabling back propagation training of memristor crossbar neuromorphic processors

2. On Langevin Updating in Multilayer Perceptrons

3. Strategies to Improve the Accuracy of Memristor-Based Convolutional Neural Networks

4. Deep learning with limited numerical precision;gupta;Proc Int Conf Mach Learn,2015

5. Novel Method Enabling Forward and Backward Propagations in NAND Flash Memory for On-Chip Learning

Cited by 1 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. Simulation of In-Situ Training in Spike Neural Network Based on Non-Ideal Memristors;IEEE Journal of the Electron Devices Society;2023

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