• DocumentCode
    742257
  • Title

    Uncertain<T>: Abstractions for Uncertain Hardware and Software

  • Author

    Bornholt, James ; Mytkowicz, Todd ; McKinley, Kathryn S.

  • Volume
    35
  • Issue
    3
  • fYear
    2015
  • Firstpage
    132
  • Lastpage
    143
  • Abstract
    Building correct, efficient systems that reason about the approximations produced by sensors, machine learning, big data, humans, and approximate hardware and software requires new standards and abstractions. The Uncertain <;T>; software abstraction aims to tackle these pervasive correctness, optimization, and programmability problems and guide hardware and software designers in producing estimates.
  • Keywords
    Big Data; hardware-software codesign; learning (artificial intelligence); big data; machine learning; programmability problems; sensors; software abstraction; uncertain hardware; uncertain software; Bayes methods; Energy efficiency; Global Positioning System; Probabilistic programming; Programming; Sensors; Uncertainty; approximation; energy efficiency; estimates; probabilistic programming; programming models; uncertainty;
  • fLanguage
    English
  • Journal_Title
    Micro, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1732
  • Type

    jour

  • DOI
    10.1109/MM.2015.52
  • Filename
    7106409
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=742257