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Performance and Scalability of GPU-Based Convolutional Neural Networks

Daniel Strigl, Klaus Kofler, Stefan Podlipnig
Distrib. & Parallel Syst. Group, Univ. of Innsbruck, Innsbruck, Austria
2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing, 2010

@conference{strigl2010performance,

   title={Performance and Scalability of GPU-Based Convolutional Neural Networks},

   author={Strigl, D. and Kofler, K. and Podlipnig, S.},

   booktitle={Parallel, Distributed and Network-Based Processing (PDP), 2010 18th Euromicro International Conference on},

   pages={317–324},

   issn={1066-6192},

   year={2010},

   organization={IEEE}

}

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In this paper we present the implementation of a framework for accelerating training and classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a derivative of standard Multilayer Perceptron (MLP) neural networks optimized for two-dimensional pattern recognition problems such as Optical Character Recognition (OCR) or face detection. We describe the basic parts of a CNN and demonstrate the performance and scalability improvement that can be achieved by shifting the computation-intensive tasks of a CNN to the GPU. Depending on the network topology training and classification on the GPU performs 2 to 24 times faster than on the CPU. Furthermore, the GPU version scales much better than the CPU implementation with respect to the network size.
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