Speed-Up Method for Neural Network Learning Using GPGPU

机译：使用GPGPU进行神经网络学习的加速方法

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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.

机译：GPU是绘制图形的专用电路，因此它具有可以实现许多简单算术电路的特征。希望该特性不仅可以应用于图形处理，还可以应用于大规模并行处理。本文将神经网络作为模式识别算法之一，通过使用GPU来使其更快。在神经网络的学习中，有许多要同时处理的点。我们提出了一种使神经网络在三点上并行化的方法。在具有不同初始权重系数，学习模式或神经网络层中的神经元的神经网络中实现并行化。这些方法可以组合使用，但是第一种方法可以独立处理。因此，采用三种方法中的一种（第一种方法）作为比较，以与提出的方法进行比较。结果，所提出的方法比比较方法快6倍。

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来源
《Distributed computing and artificial intelligence.》|2012年|p.73-80|共8页
会议地点 Salamanca(ES);Salamanca(ES)
作者
Yuta Tsuchida; Michifumi Yoshioka; Sigeru Omatu;
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作者单位

Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan;

Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan;

Department of Electronics, Information and Communication Engineering Faculty of Engineering, Oska Institute of Technology,Omiya, Asahiku, Osaka, 535-8585, Japan;

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原文格式 PDF
正文语种 eng
中图分类人工智能理论;人工智能理论;
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Speed-Up Method for Neural Network Learning Using GPGPU

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