• DocumentCode
    3179659
  • Title

    Exploiting Bayesian Belief Network for Adaptive IP-Reuse Decision

  • Author

    Azman, A.W. ; Bigdeli, A. ; Biglari-Abhari, M. ; Mustafah, Y.M. ; Lovell, B.C.

  • Author_Institution
    Sch. of ITEE, Univ. of Queensland, Brisbane, QLD, Australia
  • fYear
    2009
  • fDate
    1-3 Dec. 2009
  • Firstpage
    66
  • Lastpage
    73
  • Abstract
    A smart camera processor has to perform substantial amount of processing of data-intensive operations. Hence, it is vital to identify critical segments of the processing load by involving HW/SW codesign in smart camera system design. This paper presents a novel fully automatic hybrid framework that combines heuristic and knowledge-based approaches to partition, allocate and schedule IP modules efficiently. In this work, the concept of Bayesian Belief Network (BBN) is utilised and incorporated into the proposed framework. In the experiment section of this paper, we report a comparison of our proposed framework with three previously published work: A BBN based method proposed by a research group from the University of Arizona, the exhaustive algorithm and finally the with greedy algorithms.
  • Keywords
    belief networks; cameras; greedy algorithms; hardware-software codesign; image processing; Bayesian belief network; HW/SW codesign; adaptive IP-reuse decision; data processing; greedy algorithms; smart camera system design; Adaptive systems; Application software; Artificial intelligence; Artificial neural networks; Australia; Bayesian methods; Digital images; Field programmable gate arrays; Hardware; Smart cameras; bayesian belief network; codesign; partitioning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Image Computing: Techniques and Applications, 2009. DICTA '09.
  • Conference_Location
    Melbourne, VIC
  • Print_ISBN
    978-1-4244-5297-2
  • Electronic_ISBN
    978-0-7695-3866-2
  • Type

    conf

  • DOI
    10.1109/DICTA.2009.21
  • Filename
    5384971