Abstract
Convolutional neural networks (CNNs) are state-of-the-art machine learning algorithm in low-resolution vision tasks and are widely applied in many applications. However, the training process of them is very time-consuming. As a result, many approaches have been proposed in which parallelization is one of the most effective. In this article, we parallelized a classic CNN on a new platform of Intel\(^{{\textregistered }}\) Xeon Phi\(^{{{\text {TM}}}}\) Coprocessor with OpenMP. Our implementation acquired 131\(\times \) speedup against the serial version running on the coprocessor itself and 8.3\(\times \) speedup against the serial baseline on the Xeon\(^{{\textregistered }}\) E5-2697 CPU.
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References
Osadchy, M., Cun, Y.L., Miller, M.L.: Synergistic face detection and pose estimation with energy-based models. The Journal of Machine Learning Research 8, 1197–1215 (2007)
Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y.: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Networks 16(5), 555–559 (2003)
Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649. IEEE (June 2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Scherer, D., Schulz, H., Behnke, S.: Accelerating large-scale convolutional neural networks with parallel graphics multiprocessors. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 82–91. Springer, Heidelberg (2010)
Huqqani, A.A., Schikuta, E., Ye, S., Chen, P.: Multicore and gpu parallelization of neural networks for face recognition. Procedia Computer Science 18, 349–358 (2013)
Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160(1), 106 (1962)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 92–101. Springer, Heidelberg (2010)
LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 9–48. Springer, Heidelberg (2012)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: 2013 12th International Conference on Document Analysis and Recognition, vol. 2, pp. 958–958. IEEE Computer Society (August 2003)
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Liu, J., Wang, H., Wang, D., Gao, Y., Li, Z. (2015). Parallelizing Convolutional Neural Networks on Intel\(^{\textregistered }\) Many Integrated Core Architecture. In: Pinho, L., Karl, W., Cohen, A., Brinkschulte, U. (eds) Architecture of Computing Systems – ARCS 2015. ARCS 2015. Lecture Notes in Computer Science(), vol 9017. Springer, Cham. https://doi.org/10.1007/978-3-319-16086-3_6
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DOI: https://doi.org/10.1007/978-3-319-16086-3_6
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