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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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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