Deep Learning Video Analytics Through Edge Computing and Neural Processing Units on Mobile Devices

Tianxiang Tan, Guohong Cao

Research output: Contribution to journalArticlepeer-review

Abstract

Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices. To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study three problems: \textit{Max-Accuracy} where the goal is to maximize the accuracy under some time constraints, \textit{Max-Utility} where the goal is to maximize the utility which is a weighted function of processing time and accuracy, and \textit{Min-Energy} where the goal is to minimize the energy under some time and accuracy constraints. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.

Original languageEnglish (US)
JournalIEEE Transactions on Mobile Computing
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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