9594

Multicore and GPU Parallelization of Neural Networks for Face Recognition

Altaf Ahmad Huqqani, Erich Schikuta, Sicen Ye, Peng Chen
University of Vienna, Faculty of Computer Science, Wahringer Strae 29, A-1090 Vienna, Austria
Procedia Computer Science, Volume 18, Pages 349-358, 2013
@article{huqqani2013multicore,

   title={Multicore and GPU Parallelization of Neural Networks for Face Recognition},

   author={Huqqani, Altaf Ahmad and Schikuta, Erich and Ye, Sicen and Chen, Peng},

   journal={Procedia Computer Science},

   volume={18},

   pages={349–358},

   year={2013},

   publisher={Elsevier}

}

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Training of Artificial Neural Networks for large data sets is a time consuming task. Various approaches have been proposed to reduce the efforts, many of them by applying parallelization techniques. In this paper we develop and analyze two novel parallel training approaches for Backpropagation neural networks for face recognition. We focus on two specific parallelization environments, using on the one hand OpenMP on a conventional multithreaded CPU and CUDA on a GPU. Based on our findings we give guidelines for the efficient parallelization of Backpropagation neural networks on multicore and GPU architectures. Additionally, we present a traversal method finding the best combination of learning rate and momentum term by varying the number of hidden neurons supporting the parallelization efforts.
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