Implementation of Convolutional Neural Networks in Memristor Crossbar Arrays with Binary Activation and Weight Quantization-Reference-Cited by-同舟云学术

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Implementation of Convolutional Neural Networks in Memristor Crossbar Arrays with Binary Activation and Weight Quantization

Published:2024-01-01 Issue:1 Volume:16 Page:1054-1065
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Park Jinwoo¹,Kim Sungjoon²,Song Min Suk¹,Youn Sangwook¹,Kim Kyuree¹,Kim Tae-Hyeon³^ORCID,Kim Hyungjin⁴^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea

2. Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea

3. Department of Semiconductor Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea

4. Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Korea

Funder

National Research Foundation of Korea

Institute for Information and Communications Technology Promotion

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.3c13775

Reference68 articles.

1. He, K.; Zhang, X.; Ren, S.; Sun, J.Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016; pp 770–778.

2. Convolutional Neural Networks for Speech Recognition

3. An Analysis Of Convolutional Neural Networks For Image Classification

4. Gradient-based learning applied to document recognition

5. 3-bit multilevel operation with accurate programming scheme in TiO x /Al2O3 memristor crossbar array for quantized neuromorphic system

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