Improving Error Resilience Analysis Methodology of Iterative Workloads for Approximate Computing

G.A. Gillani, Andre B.J. Kokkeler

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    7 Citations (Scopus)
    4 Downloads (Pure)

    Abstract

    Assessing error resilience inherent to the digital processing workloads provides application-specific insights towards approximate computing strategies for improving power efficiency and/or performance. With the case study of radio astronomy calibration, our contributions for improving the error resilience analysis are focused primarily on iterative methods that use a convergence criterion as a quality metric to terminate the iterative computations. We propose an adaptive statistical approximation model for high-level resilience analysis that provides an opportunity to divide a workload into exact and approximate iterations. This improves the existing error resilience analysis methodology by quantifying the number of approximate iterations (23% of the total iterations in our case study) in addition to other parameters used in the state-of-the-art techniques.This way heterogeneous architectures comprised of exact and inexact computing cores and adaptive accuracy architectures can be exploited efficiently. Moreover, we demonstrate the importance of quality function reconsideration for convergence based iterative processes as the original quality function (the convergence criterion) is not necessarily sufficient in the resilience analysis phase. If such is the case, an additional quality function has to be defined to assess the viability of the approximate techniques.
    Original languageEnglish
    Title of host publicationCF'17
    Subtitle of host publicationProceedings of the Computing Frontiers Conference
    PublisherAssociation for Computing Machinery (ACM)
    Pages374-379
    Number of pages6
    ISBN (Print)978-1-4503-4487-6/17/05
    DOIs
    Publication statusPublished - 15 May 2017
    EventACM International Conference on Computing Frontiers 2017 - University of Siena, Palazzo del Rettorato, Siena, Italy
    Duration: 15 May 201717 May 2017
    http://www.computingfrontiers.org/2017/

    Conference

    ConferenceACM International Conference on Computing Frontiers 2017
    CountryItaly
    CitySiena
    Period15/05/1717/05/17
    Internet address

    Keywords

    • Error resilience analysis
    • iterative workloads
    • quality function
    • approximate computing
    • heterogeneous architectures

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