Publication Type

Conference Paper

Publication Date

6-2017

Abstract

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.

Keywords

Continuous vision, Deep learning, Mobile GPU, Mobile sensing

Discipline

Computer and Systems Architecture | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

MobiSys 2017: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, Niagara Falls; United States, 2017 June 19-23

Identifier

10.1145/3081333.3081360

Publisher

USPTO

City or Country

Niagara Falls; United States

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1145/3081333.3081360

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