Accelerating CNN’s forward process on mobile GPU using OpenCL

Yang Shi; Qiang Lan; Hao Fang; Mei Wen

doi:10.1117/12.2244914

29 August 2016 Accelerating CNN’s forward process on mobile GPU using OpenCL

Yang Shi, Qiang Lan, Hao Fang, Mei Wen

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100334W (2016) https://doi.org/10.1117/12.2244914
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

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.

Citation Download Citation

Yang Shi, Qiang Lan, Hao Fang, and Mei Wen "Accelerating CNN’s forward process on mobile GPU using OpenCL", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334W (29 August 2016); https://doi.org/10.1117/12.2244914

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