Performance and Scalability of GPU-Based Convolutional Neural Networks
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}
}
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.
March 8, 2011 by hgpu
