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

Autor:	Oskouei, Seyyed Salar Latifi, Golestani, Hossein, Hashemi, Matin, Ghiasi, Soheil
Rok vydání:	2015
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Computer Vision and Pattern Recognition
Zdroj:	Proceedings of the 2016 ACM Multimedia Conference, Open Source Software Track, pages 1201-1205, October 2016
Druh dokumentu:	Working Paper
DOI:	10.1145/2964284.2973801
Popis:	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 https://github.com/ENCP/CNNdroid
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1511.07376 Zobrazit plný text záznamu View this record from Arxiv