An in-memory computing multiply-and-accumulate circuit based on ternary STT-MRAMs for convolutional neural networks-Reference-Cited by-同舟云学术

An in-memory computing multiply-and-accumulate circuit based on ternary STT-MRAMs for convolutional neural networks

Published:2022-10-25 Issue:20 Volume:19 Page:20220399-20220399
ISSN:1349-2543
Container-title:IEICE Electronics Express
language:en
Short-container-title:IEICE Electron. Express

Author:

Zhao Guihua¹,Jin Xing¹,Ye Huafeng²,Peng Yating²,Liu Wei¹,Yin Ningyuan²,Chen Weichong²,Chen Jianjun¹,Li Ximing¹,Yu Zhiyi³

Affiliation:

1. School of Electronics and Information Technology, Sun Yat-sen University

2. School of Microelectronics Science and Technology, Sun Yat-sen University

3. Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems, Sun Yat-Sen University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Link

https://www.jstage.jst.go.jp/article/elex/19/20/19_19.20220399/_pdf

Reference32 articles.

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2. [2] R. Girshick: “Fast R-CNN,” 2015 IEEE International Conference on Computer Vision (ICCV) (2015) 1440 (DOI: 10.1109/ICCV.2015.169).

3. [3] R. Girshick, et al.: “Rich feature hierarchies for accurate object detection and semantic segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition (2014) 580 (DOI: 10.1109/CVPR.2014.81).

4. [4] A. Krizhevsky, et al.: “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems 25 (2012) (DOI: 10.1145/3065386).

5. [5] L. Yann, et al.: “Deep learning,” Nature 521 (2015) 436 (DOI: 10.1038/nature14539).

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