Advertisement

Performance Analysis of Parallel and Scalable GPU Based Convolutional Neural Network

  • Umesh ChavanEmail author
  • Dinesh Kulkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1025)

Abstract

Convolutional Neural networks (CNN) have succeeded great impact in various tasks of machine learning. Training CNN model is a computationally intensive task. Scalability and performance of CNN with GPU is demonstrated in this study using CUDA (Compute Unified Device Architecture) framework. We evaluated performance characteristics using our own designed CNN model. The model is configured for Facial expression Recognition classification task. The novelty of this effort is to demonstrate performance acceleration in scalable CNN. The parallel tasks using the hardware feature of general-purpose- computing on graphics processing unit (GPGPU) has been shown to be appropriate to be applied to CNN. We implemented at multi-node distributed training; which allows us to efficiently parallelize deep networks across multiple servers, in order to minimize the time to train. The experiment result shows that the proposed experiment in this study gained over 7 times speedup.

Keywords

Deep learning CNN Graphical processing unit 

References

  1. 1.
    LeCun, Y., Bengio, Y.: Pattern recognition and Neural Networks. In: Arbib, M.A. (ed.) Handbook of Brain Theory and Neural Networks. MIT Press (1995)Google Scholar
  2. 2.
    Chellapilla, K., Puri, S., Simard, P.: High performance convolutional neural networks for document processing. In: Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (2006)Google Scholar
  3. 3.
    Li, X., et al.: Performance analysis of GPU-based convolutional neural networks. In: 45th International Conference on Parallel Processing (2016)Google Scholar
  4. 4.
    Arriaga, O., Valdenegro-Toro, M., Plöger, P.: Real-time convolutional neural networks for emotion and gender classification. arXiv preprint arXiv:1710.07557 (2017)
  5. 5.
    Guo, Y., Tao, D., Xiang, H.: Deep neural networks with relativity learning for facial expression recognition. In: IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (2016)Google Scholar
  6. 6.
    Huang, Y., Li, K., Wang, G., Cao, M., Li, P., Zhang, Y.: Recognition of convolutional neural network based on CUDA technology (2015). Available https://arxiv.org/abs/1506.00074 [Online]
  7. 7.
    Adam, B., Zaman, F.H.K., Yassin, I.M., Abidin, H.Z.: Faster R-CNN Implementation using CUDA Architecture in GeForce GTX 10 Series (2016)Google Scholar
  8. 8.
    Choi, S., Lee, K.: A CUDA-based implementation of convolutional neural network. In: 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Kuta Bali (2017)Google Scholar
  9. 9.
    Stephan, P., Daniel, S., Klaus, K.: Performance and scalability of GPU based convolutional neural networks. IEEE (2010)Google Scholar
  10. 10.
    Corrado, G., Monga, R., Chen, K., Devin, M., Senior, A., Tucker, P.: Large scale distributed deep networks. In: NIPS (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Walchand College of EngineeringSangliIndia

Personalised recommendations