Special session: A qantifiable approach to approximate computing

Chaofan Li, Wenbin Xu, Deepashree Sengupta, Jiang Hu, Farhana Sharmin Snigdha, Sachin S. Sapatnekar

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

1 Scopus citations

Abstract

Approximate computing has applications in areas such as image processing, neural computation, distributed systems, and real-time systems, where the results may be acceptable in the presence of controlled levels of error. The promise of approximate computing is in its ability to render just enough performance to meet quality constraints. However, going from this theoretical promise to a practical implementation requires a clear comprehension of the system requirements and matching them to the design of approximations as the system is implemented. This involves the tasks of (a) identifying the design space of potential approximations, (b) modeling the injected error as a function of the level of approximation, and (c) optimizing the system over the design space to maximize a metric, typically the power savings, under constraints on the maximum allowable degradation. Often, the error may be introduced at a low level of design (e.g., at the level of a full adder) but its impact must be percolated up to system-level error metrics (e.g., PSNR in a compressed image), and a practical approach must devise a coherent and quantifiable way of translating between error/power tradeoffs at all levels of design.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion, CASES 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450351843
DOIs
StatePublished - Oct 15 2017
Event2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems, CASES 2017 - Seoul, Korea, Republic of
Duration: Oct 15 2017Oct 20 2017

Publication series

NameProceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion, CASES 2017

Other

Other2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems, CASES 2017
CountryKorea, Republic of
CitySeoul
Period10/15/1710/20/17

Bibliographical note

Funding Information:
∗This work was supported in part by the NSF under awards CCF-1162267, CCF-1525925, and CCF-1525749.

Keywords

  • Analysis
  • Approximate computing
  • Error resilience
  • Optimization

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