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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:1511.07376 (cs)
[Submitted on 23 Nov 2015 (v1), last revised 15 Oct 2016 (this version, v2)]

Title:CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android

Authors:Seyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi, Soheil Ghiasi
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Abstract:Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However, performance and energy consumption limitations make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPU-accelerated library, dubbed CNNdroid, for execution of trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X speedup and 130X energy saving on current mobile devices. The CNNdroid open source library is available for download at this https URL
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1511.07376 [cs.DC]
  (or arXiv:1511.07376v2 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.1511.07376
Journal reference: Proceedings of the 2016 ACM Multimedia Conference, Open Source Software Track, pages 1201-1205, October 2016
Related DOI: https://doi.org/10.1145/2964284.2973801

Submission history

From: Matin Hashemi [view email]
[v1] Mon, 23 Nov 2015 19:32:37 UTC (1,135 KB)
[v2] Sat, 15 Oct 2016 19:22:46 UTC (494 KB)
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Seyyed Salar Latifi Oskouei
Hossein Golestani
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