A RRAM based Max-Pooling Scheme for Convolutional Neural Network-Reference-Cited by-全球学者库

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A RRAM based Max-Pooling Scheme for Convolutional Neural Network

Published:2021-04-08 Issue: Volume: Page:
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Container-title:2021 5th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)
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Author:

Ling Yaotian,Wang Zongwei,Yang Yunfan,Yu Zhizhen,Zheng Qilin,Qin Yabo,Cai Yimao,Huang Ru

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9420829/9420830/09421061.pdf?arnumber=9421061

Cited by 5 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. Machine Learning-Based Crowd behavior Analysis and Forecasting;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2023-06-01

2. Ultra-High-Speed Accelerator Architecture for Convolutional Neural Network Based on Processing-in-Memory Using Resistive Random Access Memory;Sensors;2023-02-21

3. Energy Efficient Spin-Based Implementation of Neuromorphic Functions in CNNs;IEEE Open Journal of Nanotechnology;2023

4. Efficient Implementation of Max-Pooling Algorithm Exploiting History-Effect in Ferroelectric-FinFETs;IEEE Transactions on Electron Devices;2022-11

5. RBC Classification using Deep Learning;2021 19th OITS International Conference on Information Technology (OCIT);2021-12

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