Operation Verification of Deep Learning Applications on Small Computers

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  • Nishizaki Hiromitsu
    The Graduate School of Interdisciplinary Research, University of Yamanashi
  • Leow Chee Siang
    Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi
  • Makino Koji
    The Graduate School of Interdisciplinary Research, University of Yamanashi

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Other Title
  • 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
  • コガタ コンピュータ ニ オケル シンソウ ガクシュウ アプリケーション ノ ドウサ ケンショウ

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Abstract

<p>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.</p>

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