Deep Learning Video Analytics on Edge Computing Devices

Tianxiang Tan, Guohong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The rapid progress of deep learning-based techniques such as Convolutional Neural Network (CNN) has enabled many emerging applications related to video analytics and running them on mobile devices can help improve our daily lives in many ways. However, there are many challenges for video analytics on mobile devices using multiple CNN models. CNN models are resource hungry, and each model requires a large amount of computational power and occupies a large portion of memory space. Although video processing can be offloaded to reduce the computation time, transmitting large amount of video data is time consuming. Thus, offloading is not always the best option. Moreover, different CNN models have different memory usage and processing time, making the scheduling problem more complex. As a result, besides deciding which task to be offloaded, we must decide which CNN model should reside in the memory and for how long, and which CNN model should be switched out due to memory constraint. In this paper, we propose resource aware scheduling algorithms to address these challenges. We identify the task scheduling problem for running multiple CNN models on mobile devices under resource constraints and formulate it as an integer programming problem. We propose resource-Aware scheduling algorithms which combine offloading and local processing methods to minimize the completion time of video processing. We implement the proposed scheduling algorithms on Android-based smartphones and demonstrate its effectiveness through extensive experiments.

Original languageEnglish (US)
Title of host publication2021 18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665441087
DOIs
StatePublished - Jul 6 2021
Event18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021 - Virtual, Online
Duration: Jul 6 2021Jul 9 2021

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
Volume2021-July
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021
CityVirtual, Online
Period7/6/217/9/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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