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GPU-enabled back-propagation artificial neural network for digit recognition in parallel

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Abstract

In this paper, we show that the GPU (graphics processing unit) can be used not only for processing graphics, but also for high speed computing. We provide a comparison between the times taken on the CPU and GPU to perform the training and testing of a back-propagation artificial neural network. We implemented two neural networks for recognizing handwritten digits; one consists of serial code executed on the CPU, while the other is a GPU-based version of the same system which executes in parallel. As an experiment for performance evaluation, a system for neural network training on the GPU is developed to reduce training time. The programming environment that the system is based on is CUDA which stands for compute unified device architecture, which allows a programmer to write code that will run on an NVIDIA GPU card. Our results over an experiment of digital image recognition using neural network confirm the speed-up advantages by tapping on the resources of GPU. Our proposed model has an advantage of simplicity, while it shows on par performance with the state-of-the-arts algorithms.

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Acknowledgments

The authors are thankful for the financial support from the research grant “Peer-production approaches to e-Learning (PPAeL),” Grant No. FDCT 019/2011/A1, offered by Macau Fundo para o Desenvolvimento das Ciências e da Tecnologia.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau SAR, China

    Ricardo Brito & Simon Fong

  2. Department of Computer and Multimedia Engineering, Dongguk University, Seoul, Korea

    Kyungeun Cho

  3. College of Information Engineering, North China University of Technology, Beijing, China

    Wei Song

  4. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Raymond Wong

  5. Department of Computer Science, Lakehead University, Thunder Bay, Canada

    Sabah Mohammed & Jinan Fiaidhi

Authors
  1. Ricardo Brito
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  2. Simon Fong
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  3. Kyungeun Cho
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  4. Wei Song
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  5. Raymond Wong
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  6. Sabah Mohammed
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  7. Jinan Fiaidhi
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Correspondence to Simon Fong.

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Brito, R., Fong, S., Cho, K. et al. GPU-enabled back-propagation artificial neural network for digit recognition in parallel. J Supercomput 72, 3868–3886 (2016). https://doi.org/10.1007/s11227-016-1633-y

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  • DOI: https://doi.org/10.1007/s11227-016-1633-y

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