Human emotion based real-time memory and computation management on resource-limited edge devices

Yijie Wei, Zhiwei Zhong, Jie Gu

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

Abstract

Emotional AI or Affective Computing has been projected to grow rapidly in the upcoming years. Despite many existing developments in the application space, there has been a lack of hardware-level exploitation of the user's emotions. In this paper, we propose a deep collaboration between user's affects and the hardware system management on resource-limited edge devices. Based on classification results from efficient affect classifiers on smartphone devices, novel real-time management schemes for memory, and video processing are proposed to improve the energy efficiency of mobile devices. Case studies on H.264 / AVC video playback and Android smartphone usages are provided showing significant power saving of up to 23% and reduction of memory loading of up to 17% using the proposed affect adaptive architecture and system management schemes.

Original languageEnglish (US)
Title of host publicationProceedings of the 59th ACM/IEEE Design Automation Conference, DAC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages487-492
Number of pages6
ISBN (Electronic)9781450391429
DOIs
StatePublished - Jul 10 2022
Event59th ACM/IEEE Design Automation Conference, DAC 2022 - San Francisco, United States
Duration: Jul 10 2022Jul 14 2022

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference59th ACM/IEEE Design Automation Conference, DAC 2022
Country/TerritoryUnited States
CitySan Francisco
Period7/10/227/14/22

Keywords

  • LSTM
  • affective computing
  • edge devices
  • memory management
  • system management
  • wearable devices

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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