XBioSiP: A methodology for approximate bio-signal processing at the edge

Bharath Srinivas Prabakaran, Semeen Rehman, Muhammad Shafique

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


Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-Edge (wearables) through the emerging trend of approximate computing. This paper presents XBioSiP, a novel methodology for approximate bio-signal processing that employs two quality evaluation stages, during the pre-processing and bio-signal processing stages, to determine the approximation parameters. It thereby achieves high energy savings while satisfying the user-determined quality constraint. Our methodology achieves, up to 19× and 22× reduction in the energy consumption of a QRS peak detection algorithm for 0% and < 1% loss in peak detection accuracy, respectively.

Original languageEnglish (US)
Title of host publicationProceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
StatePublished - Jun 2 2019
Event56th Annual Design Automation Conference, DAC 2019 - Las Vegas, United States
Duration: Jun 2 2019Jun 6 2019

Publication series

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


Conference56th Annual Design Automation Conference, DAC 2019
Country/TerritoryUnited States
CityLas Vegas


  • Adders
  • Approximate Computing
  • Arithmetic Units
  • Bio-Signal
  • ECG
  • Edge Computing
  • Energy-Efficiency
  • Hardware Design
  • Healthcare
  • IoT
  • Multipliers
  • Wearables

ASJC Scopus subject areas

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


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