Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2016
Abstract
Recently, a branch of machine learning algorithms called deep learning gained huge attention to boost up accuracy of a variety of sensing applications. However, execution of deep learning algorithm such as convolutional neural network on mobile processor is non-trivial due to intensive computational requirements. In this paper, we present our early design of DeepSense - a mobile GPU-based deep convolutional neural network (CNN) framework. For its design, we first explored the differences between server-class and mobile-class GPUs, and studied effectiveness of various optimization strategies such as branch divergence elimination and memory vectorization. Our results show that DeepSense is able to execute a variety of CNN models for image recognition, object detection and face recognition in soft real time with no or marginal accuracy tradeoffs. Experiments also show that our framework is scalable across multiple devices with different GPU architectures (e.g. Adreno and Mali).
Keywords
Deep learning, Mobile GPU, Mobile sensing application
Discipline
Computer Sciences | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
WearSys'16: Proceedings of the 2016 Workshop on Wearable Systems and Applications: June 30, 2016, Singapore
First Page
25
Last Page
30
ISBN
9781450343268
Identifier
10.1145/2935643.2935650
Publisher
ACM
City or Country
New York
Citation
HUYNH NGUYEN LOC; BALAN, Rajesh Krishna; and LEE, Youngki.
DeepSense: A GPU-based deep convolutional neural network framework on commodity mobile devices. (2016). WearSys'16: Proceedings of the 2016 Workshop on Wearable Systems and Applications: June 30, 2016, Singapore. 25-30.
Available at: https://ink.library.smu.edu.sg/sis_research/3276
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/2935643.2935650