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小型コンピュータにおける深層学習アプリケーションの動作検証

小型コンピュータにおける深層学習アプリケーションの動作検証

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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: 2018/09/01

タイトル(英語): Operation Verification of Deep Learning Applications on Small Computers

著者名: 西崎 博光(山梨大学大学院総合研究部),レオ チー シャン(山梨大学 大学院医工農学総合教育部),牧野 浩二(山梨大学大学院総合研究部)

著者名(英語): Hiromitsu Nishizaki (The Graduate School of Interdisciplinary Research, University of Yamanashi), Chee Siang Leow (Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi), Koji Makino (The Graduate School o

キーワード: 深層学習,小型コンピュータ,実行時間,消費メモリ量,パラメータ数  deep learning,small computer,execution time,consumption memory,the number of parameters

要約(英語): Recently, deep learning technologies have been in the spotlight. Deep learning is one of a powerful technology to classify or recognize objects which captured by a camera. Such application has a high affinity with Internet-of-Things (IoT) devices. Therefore, it is considered that these technologies are used in embedded systems and IoT devices. In this paper, we verify deep learning applications like image classification can work well on a small computer such as Raspberry Pi. We develop three deep learning applications by using two types of deep learning frameworks (libraries). We prepare four types of small computers, and these applications are tested on the computers. In addition, we also investigate the relationship between the processing time, the memory consumption and the number of parameters of the deep neural network model. The verification experiments show that a program based on a deep learning library implemented by C++ language fast run and simple neural network models could work in real-time on small computers. Besides, the other experiment also clears that the more parameters increase the processing time and the consumption memory in proportion without depending on the deep learning libraries and small computers.

本誌: 電気学会論文誌C(電子・情報・システム部門誌) Vol.138 No.9 (2018) 特集Ⅰ:知能メカトロニクス分野と連携する知覚情報技術 特集Ⅱ:国際会議ICESS 2017

本誌掲載ページ: 1108-1115 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/138/9/138_1108/_article/-char/ja/

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