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Artificial neural network computation on graphic process unit

Zhongwen Luo, Hongzhi Liu, Xincai Wu
Faculty of Information, China University of Geoscience(Wuhan), Wuhan 430074,China
Neural Networks, 2005. IJCNN ’05. Proceedings. 2005 IEEE International Joint Conference on In Neural Networks, 2005. IJCNN ’05. Proceedings. 2005 IEEE International Joint Conference on, Vol. 1 (2005), pp. 622-626 vol. 1.

@conference{luo2005artificial,

   title={Artificial neural network computation on graphic process unit},

   author={Luo, Z. and Liu, H. and Wu, X.},

   booktitle={Neural Networks, 2005. IJCNN’05. Proceedings. 2005 IEEE International Joint Conference on},

   volume={1},

   pages={622–626},

   isbn={0780390482},

   year={2005},

   organization={IEEE}

}

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Artificial neural network (ANN) is widely used in pattern recognition related area. In some case, the computational load is very heavy, in other case, real time process is required. So there is a need to apply a parallel algorithm on it, and usually the computation for ANN is inherently parallel. In this paper, graphic hardware is used to speed up the computation of ANN. In recent years, graphic processing unit (GPU) grows faster than CPU. Graphic hardware venders provide programmability on GPU. In this paper, application of commodity available GPU for two kinds of ANN models was explored. One is the self-organizing maps (SOM); the other is multi layer perceptron (MLP). The computation result shows that ANN computing on GPU is much faster than on standard CPU when the neural network is large. And some design rules for improve the efficiency on GPU are given.
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