An energy-efficient coarse grained spatial architecture for convolutional neural networks AlexNet-Reference-Cited by-同舟云学术

An energy-efficient coarse grained spatial architecture for convolutional neural networks AlexNet

Published:2017 Issue:15 Volume:14 Page:20170595-20170595
ISSN:1349-2543
Container-title:IEICE Electronics Express
language:en
Short-container-title:IEICE Electron. Express

Author:

Zhao Boya¹,Wang Mingjiang¹,Liu Ming¹

Affiliation:

1. Harbin Institute of Technology

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/14/15/14_14.20170595/_pdf

Reference17 articles.

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2. [2] K. Simonyan and A. Zisserman: “Very deep convolutional networks for large-scale image recognition,” CoRR, vol. abs/1409.1556 (2014).

3. [3] C. Szegedy, et al.: “Going deeper with convolutions,” IEEE CVPR (2015) (DOI: 10.1109/CVPR.2015.7298594).

4. [4] K. He, et al.: “Deep residual learning for image recognition,” IEEE CVPR (2016) (DOI: 10.1109/CVPR.2016.90).

5. [5] R. Girshick, et al.: “Rich feature hierarchies for accurate object detection and semantic segmentation,” IEEE CVPR (2014) (DOI: 10.1109/CVPR.2014.81).

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