Seyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi, Soheil Ghiasi, " CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android", Proceedings of the 24th ACM international conference on Multimedia, 2016.
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
You may access the source code from GitHub.
Download the presentation slides from here.
Use the following entry to cite our work in your publications:
@inproceedings{cnndroid2016,
author = {Latifi Oskouei, Seyyed Salar and Golestani, Hossein and Hashemi, Matin and Ghiasi, Soheil},
title = {CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android},
booktitle = {Proceedings of the 2016 ACM on Multimedia Conference},
series = {MM '16},
year = {2016},
location = {Amsterdam, The Netherlands},
pages = {1201--1205}
}