カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2011/11/01タイトル(英語): Speed up Neural Network Learning by GPGPU著者名: 土田 悠太（大阪府立大学 大学院工学研究科 知能情報工学分野），吉岡 理文（大阪府立大学 大学院工学研究科 知能情報工学分野）著者名(英語): Yuta Tsuchida (Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University), Michifumi Yoshioka (Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University)キーワード: GPGPU，ニューラルネットワーク，CUDA GPGPU，Neural Network，CUDA要約(英語): Recently, graphic boards have higher performance with development of 3DCG and movie processing than CPU, and widely used with progress of computer entertainment. Implementation of the General-purpose computing on GPU (GPGPU) become more easier by the integrated development environment, CUDA distributed by NVIDIA. GPU has dozens or a hundred arithmetic circuits, whose allocations are controlled by CUDA. In the previous researches, the implementation of the neural network using GPGPU have been studied, however the learning of networks was not mentioned because the GPU performance is low in conditional processing whereas high in linear algebra processing. Therefore we have proposed two methods. At first, a whole network is implemented as a thread, and some networks are taught in parallel to shorten the time necessary to find the optimal weight coefficients. Secondly, this paper introduces parallelization in the neural network structure, that is, the calculation of neurons in the same layers can be paralleled. And the processes to teach for same network with different patterns are independent also. As a result, the second method is 20 times faster than CPU, and compared with the first proposed method, that is about 6 times faster.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.131 No.11 （2011） 特集：電気関係学会関西連合大会本誌掲載ページ: 1889-1894 p原稿種別: 論文／日本語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/131/11/131_11_1889/_article/-char/ja/
