On Effective and Efficient Quality Management for Approximate Computing

Ting Wang, Qian Zhang, Nam Sung Kim, Qiang Xu

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

Abstract

Approximate computing, where computation quality is traded off for better performance and/or energy savings, has gained significant tractions from both academia and industry. With approximate computing, we expect to obtain acceptable results, but how do we make sure the quality of the final results are acceptable? This challenging problem remains largely unexplored. In this paper, we propose an effective and efficient quality management framework to achieve controlled quality-efficiency tradeoffs. To be specific, at the offline stage, our solution automatically selects an appropriate approximator configuration considering rollback recovery for large occasional errors with minimum cost under the target quality requirement. Then during the online execution, our framework judiciously determines when and how to rollback, which is achieved with cost-effective yet accurate quality predictors that synergistically combine the outputs of several basic light-weight predictors. Experimental results demonstrate that our proposed solution can achieve 11% to 23% energy savings compared to existing solutions under the target quality requirement.

Original languageEnglish (US)
Title of host publicationISLPED 2016 - Proceedings of the 2016 International Symposium on Low Power Electronics and Design
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages156-161
Number of pages6
ISBN (Electronic)9781450341851
DOIs
StatePublished - Aug 8 2016
Event21st IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2016 - San Francisco, United States
Duration: Aug 8 2016Aug 10 2016

Publication series

NameProceedings of the International Symposium on Low Power Electronics and Design
ISSN (Print)1533-4678

Other

Other21st IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2016
CountryUnited States
CitySan Francisco
Period8/8/168/10/16

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Keywords

  • Approximate Computing
  • Quality Management

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Wang, T., Zhang, Q., Kim, N. S., & Xu, Q. (2016). On Effective and Efficient Quality Management for Approximate Computing. In ISLPED 2016 - Proceedings of the 2016 International Symposium on Low Power Electronics and Design (pp. 156-161). (Proceedings of the International Symposium on Low Power Electronics and Design). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/2934583.2934608