The rapid emergence of head-mounted devices such as the Microsoft Holo-lens enables a wide variety of continuous vision applications. Such applications often adopt deep-learning algorithms such as CNN and RNN to extract rich contextual information from the first-person-view video streams. Despite the high accuracy, use of deep learning algorithms in mobile devices raises critical challenges, i.e., high processing latency and power consumption. In this paper, we propose DeepMon, a mobile deep learning inference system to run a variety of deep learning inferences purely on a mobile device in a fast and energy-efficient manner. For this, we designed a suite of optimization techniques to efficiently offload convolutional layers to mobile GPUs and accelerate the processing; note that the convolutional layers are the common performance bottleneck of many deep learning models. Our experimental results show that DeepMon can classify an image over the VGG-VeryDeep-16 deep learning model in 644ms on Samsung Galaxy S7, taking an important step towards continuous vision without imposing any privacy concerns nor networking cost.
Continuous vision, Deep learning, Mobile GPU, Mobile sensing
Computer and Systems Architecture | Software Engineering
Software and Cyber-Physical Systems
MobiSys 2017: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, Niagara Falls; United States, 2017 June 19-23
City or Country
Niagara Falls; United States
HUYNH, Nguyen Loc; LEE, Youngki; and BALAN, Rajesh Krishna.
DeepMon: Mobile GPU-based deep learning framework for continuous vision applications. (2017). MobiSys 2017: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, Niagara Falls; United States, 2017 June 19-23. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3671
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