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http://dx.doi.org/10.6109/jkiice.2017.21.6.1143

Efficient Implementation of Convolutional Neural Network Using CUDA  

Ki, Cheol-Min (Department of Computer Engineering, Korea University of Technology and Education)
Cho, Tai-Hoon (School of Computer Science and Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.6, 2017 , pp. 1143-1148 More about this Journal
Abstract
Currently, Artificial Intelligence and Deep Learning are rising as hot social issues, and these technologies are applied to various fields. A good method among the various algorithms in Artificial Intelligence is Convolutional Neural Networks. Convolutional Neural Network is a form that adds Convolution Layers to Multi Layer Neural Network. If you use Convolutional Neural Networks for small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning should take long time when the size of the learning data is large and the structure of layers is complicated. In these cases, GPU-based parallel processing is frequently needed. In this paper, we developed Convolutional Neural Networks using CUDA, and show that its learning is faster and more efficient than learning using some other frameworks or programs.
Keywords
Convolutional Neural Network; Machine Learning; Parallel Processing; CUDA; Fast Speed;
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