Exploration of design space and runtime optimization for affective computing in machine learning empowered ultra-low power SoC

Yijie Wei, Kofi Otseidu, Jie Gu

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

Abstract

The incorporation of artificial intelligence into the rapidly growing IoT devices demands a high level of built-in intelligence, e.g. machine learning capability at the device level. Affective computing offers a new degree of cognitive intelligence into edge processing IoT devices by inferring human emotion, stress levels for intelligent human assistance. This work explores the design space and runtime optimization opportunity for affective computing at the system-on-chip (SoC) level. A design optimization methodology for the neural network classifier and runtime power management schemes are proposed to achieve high energy efficiency on embedded low power devices. A test chip based on a 65nm CMOS process was used to demonstrate the proposed methodology on emotion and stress classification for affective computing. An average power saving of 45% is achieved with a peak power savings of 60% from the proposed emotion-driven adaptive power management scheme.

Original languageEnglish (US)
Title of host publication2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
DOIs
StatePublished - Jul 2020
Event57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States
Duration: Jul 20 2020Jul 24 2020

Publication series

NameProceedings - Design Automation Conference
Volume2020-July
ISSN (Print)0738-100X

Conference

Conference57th ACM/IEEE Design Automation Conference, DAC 2020
CountryUnited States
CityVirtual, San Francisco
Period7/20/207/24/20

Keywords

  • Affective Computing
  • Embedded Device
  • Internet-of-Things
  • Neural Network Accelerator
  • Stress and Mood Classification

ASJC Scopus subject areas

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

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