Phase-aware optimization in approximate computing

Subrata Mitra, Manish K. Gupta, Sasa Misailovic, Saurabh Bagchi

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

Abstract

This paper shows that many applications exhibit execution-phase-specific sensitivity towards approximation of the internal subcomputations. Therefore, approximation in certain phases can be more beneficial than others. Further, this paper presents Opprox, a novel system for application's execution-phase-aware approximation. For a user provided error budget and target input parameters, Opprox identifies different program phases and searches for profitable approximation settings for each phase of the application execution. Our evaluation with five benchmarks and four existing transformations show that our phase-aware optimization on average does 14% less work for a 5% error tolerance bound and 42% less work for a 20% tolerance bound.

Original languageEnglish (US)
Title of host publicationCGO 2017 - Proceedings of the 2017 International Symposium on Code Generation and Optimization
EditorsVijay Janapa Reddi, Aaron Smith, Lingjia Tang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185-196
Number of pages12
ISBN (Electronic)9781509049318
DOIs
StatePublished - Feb 23 2017
Event2017 International Symposium on Code Generation and Optimization, CGO 2017 - Austin, United States
Duration: Feb 4 2017Feb 8 2017

Publication series

NameCGO 2017 - Proceedings of the 2017 International Symposium on Code Generation and Optimization

Other

Other2017 International Symposium on Code Generation and Optimization, CGO 2017
CountryUnited States
CityAustin
Period2/4/172/8/17

Keywords

  • Approximate Computing
  • Execution Phases

ASJC Scopus subject areas

  • Software
  • Control and Optimization

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