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An Implementation and Improvement of Convolutional Neural Networks on HSA Platform

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Data Science (ICPCSEE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 727))

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Abstract

Nowadays, the most heterogeneous architectures were made up by the various IP modules of different hardware vendors, but this model is less efficiently. In order to solve this problem, AMD joint other hardware vendors proposed heterogeneous system architecture (HSA) specification. On the one hand, the HSA could help developers to accelerate the design process and programming. On the other hand, it improved the system performance and reduced the power. In this paper we presented the implementation of a framework for accelerating training and classification of arbitrary Convolutional Neural Networks (CNNs) on the HSA, on the basis of implementation, we presented tow accelerated methods that are Online update weights and letting CPU to participate in calculation. Experimental results showed that the implementation of CNNs on HSA 4 to 10 times faster than on the CPU.

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References

  1. Yang, C., Wu, L.: GPU-based volume rendering for 3D electromagnetic environment on virtual globe. Int. J. Image Graph. Sig. Process. 2(1), 53 (2010)

    Article  Google Scholar 

  2. Harris, C.: GPU accelerated radio astronomy signal convolution. Exp. Astron. 22(1), 129–141 (2008)

    Article  Google Scholar 

  3. Michel, P., Chestnutt, J., Kagami, S., Nishiwaki, K.: GPU-accelerated real-time 3D tracking for humanoid locomotion and stair climbing. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 463–469. IEEE (2007)

    Google Scholar 

  4. Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU computing. Proc. IEEE 96(5), 879–899 (2008)

    Article  Google Scholar 

  5. Du, P., Weber, R., Luszczek, P., Tomov, S., Peterson, G., Dongarra, J.: From CUDA to OpenCL: towards a performance-portable solution for multi-platform GPU programming ☆, ☆☆. Parallel Comput. 38(8), 391–407 (2012)

    Article  Google Scholar 

  6. Blinzer, P.: The heterogeneous system architecture: it’s beyond the GPU. In: International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, p. iii. IEEE (2014). (Maxwell, J.C.: A Treatise on Electricity and Magnetism, 3rd edn., vol. 2, pp. 68–73. Clarendon, Oxford (1892))

    Google Scholar 

  7. Dan, C.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: International Conference on Document Analysis and Recognition, pp. 1135–1139. IEEE Computer Society (2011)

    Google Scholar 

  8. Lecun, B.Y., et al.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE (2010)

    Google Scholar 

  9. Ubal, R., Jang, B., Mistry, P., Schaa, D., Kaeli, D.: Multi2Sim: a simulation framework for CPU-GPU computing. In: International Conference on Parallel Architectures and Compilation Techniques, pp. 335–344. ACM (2012)

    Google Scholar 

  10. Ma, K., Li, X., Chen, W., Zhang, C.: GreenGPU: a holistic approach to energy efficiency in GPU-CPU heterogeneous architectures. In: International Conference on Parallel Processing, pp. 48–57. IEEE (2012)

    Google Scholar 

  11. Ding, J.H., Hsu, W.C., Jeng, B.C., Hung, S.H., Chung, Y.C.: HSAemu: a full system emulator for HSA platforms. In: International Conference on Hardware/Software Codesign and System Synthesis, p. 26. ACM (2014)

    Google Scholar 

  12. Luo, Z., Liu, H., Wu, X.: Artificial neural network computation on graphic process unit. In: IEEE International Joint Conference on Neural Networks, vol. 1, pp. 622–626. IEEE (2005)

    Google Scholar 

  13. Strigl, D., Kofler, K., Podlipnig, S.: Performance and scalability of GPU-based convolutional neural networks. In: Euromicro International Conference on Parallel, Distributed and Network-Based Processing, pp. 317–324. IEEE (2010)

    Google Scholar 

  14. Gadea, R., Cerdá, J., Ballester, F., Mocholí, A.: Artificial neural network implementation on a single FPGA of a pipelined on-line backpropagation, pp. 225–230 (2000)

    Google Scholar 

  15. Himavathi, S., Anitha, D., Muthuramalingam, A.: Feedforward neural network implementation in FPGA using layer multiplexing for effective resource utilization. IEEE Trans. Neural Netw. 18(3), 880 (2007)

    Article  Google Scholar 

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Acknowledgement

This research is supported by the Natural Science Foundation of BJUT, the National Natural Science Foundation of China (Grants No. 91546111, 91646201), the Key Project of Beijing Municipal Education Commission (Grants No. KZ201610005009).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Zhenshan Bao, Qi Luo & Wenbo Zhang

Authors
  1. Zhenshan Bao
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  2. Qi Luo
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  3. Wenbo Zhang
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Corresponding author

Correspondence to Zhenshan Bao .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Central South University, Changsha, China

    Min Li

  3. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Harbin University of Science and Technology, Harbin, China

    Wei Xie

  6. Harbin Sea of Clouds and Computer Technology, Harbin, China

    Zeguang Lu

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Bao, Z., Luo, Q., Zhang, W. (2017). An Implementation and Improvement of Convolutional Neural Networks on HSA Platform. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_50

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  • DOI: https://doi.org/10.1007/978-981-10-6385-5_50

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6384-8

  • Online ISBN: 978-981-10-6385-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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