Speed-Up Method for Neural Network Learning Using GPGPU
GPU is the dedicated circuit to draw the graphics, so it has a characteristic that the many simple arithmetic circuits are implemented. This characteristic is hoped to apply the massive parallelism not only graphic processing. In this paper, the neural network, one of the pattern recognition algorithms is applied to be faster by using GPU. In the learning of the neural network, there are many points to be processed at the same time. We propose a method which makes the neural network be parallelized in three points. The parallelizations are implemented in neural networks which have different initial weight coefficients, the learning patterns or neurons in a layer of neural network. These methods are used in combination, but the first method can be processed independently. Therefore one of the three methods, the first method, is employed as comparison to compare with the proposed methods. As the result, the proposed method is 6 times faster than comparison method.
KeywordsNeural Network Hide Layer Graphic Processing Unit Global Memory Neural Network Training
Unable to display preview. Download preview PDF.
- 1.Davis, C.E.: Graphics processing unit computation of neural networks. M.S. thesis, Comput. Sci. Dept., Univ. New Mexico, Albuquerque, NM (2005)Google Scholar
- 2.Kirk, D.B., Hwu, W.-M.W.: Programming Massively Parallel Processors: A Hands-on Approach. Applications of GPU Computing Series. Morgan Kaufmann (2010)Google Scholar
- 3.Jang, H., Park, A., Jung, K.: Neural Network Implementation Using CUDA and OpenMP. In: Digital Image, Computing Techniques and Applications, DICTA 2008, 155–161 (2008), doi:10.1109/DICTA.2008.82Google Scholar