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Proceedings Paper

Accelerating CNN’s forward process on mobile GPU using OpenCL
Author(s): Yang Shi; Qiang Lan; Hao Fang; Mei Wen
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Paper Abstract

The convolution neural network (CNN) is becoming more and more powerful in many areas such as image classification and speech recognition. Some projects begin to apply it on mobile phones, but often need plenty of time due to the huge amount of computation. This paper uses a deep learning framework named MXNet to realize the forward process on the mobile phone. In order to lower the time it costs, we focus on how to use the other computing device on the chip—the mobile GPU. We choose the OpenCL to offload the most time consuming layer in the CNN—convolution layer to the GPU. Besides that, this paper makes several improvements to achieve better performance and finally the experimental results demonstrate that the forward process only takes half the time in our algorithm. To the best of the authors’ knowledge, this work is the first published implantation of CNN accelerating on mobile GPU.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334W (29 August 2016); doi: 10.1117/12.2244914
Show Author Affiliations
Yang Shi, National Univ. of Defense Technology (China)
Qiang Lan, National Univ. of Defense Technology (China)
Hao Fang, National Univ. of Defense Technology (China)
Mei Wen, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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