Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices

Author:

Tu Xiaolong1ORCID,Mallik Anik2ORCID,Chen Dawei3ORCID,Han Kyungtae3ORCID,Altintas Onur4ORCID,Wang Haoxin1ORCID,Xie Jiang2ORCID

Affiliation:

1. Georgia State University, Atlanta, Georgia, USA

2. The University of North Carolina at Charlotte, Charlotte, North Carolina, USA

3. Toyota Motor North America, Mountain View, California, USA

4. Toyota Motors North America, Mountain View, California, USA

Funder

US National Science Foundation (NSF)

Toyota Motor North America

Publisher

ACM

Reference55 articles.

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2. ImageNet classification with deep convolutional neural networks

3. Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818--2826, 2016.

4. Haoxiang Li, Zhe Lin, Xiaohui Shen, Jonathan Brandt, and Gang Hua. A convolutional neural network cascade for face detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5325--5334, 2015.

5. FaceNet: A unified embedding for face recognition and clustering

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