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
Link To Document :
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