Exploiting Bayesian belief network for adaptive IP-reuse decision

Azman, A. W., Bigdeli, A., Biglari-Abhari, M., Mustafah, Y. M. and Lovell, B. C. (2009). Exploiting Bayesian belief network for adaptive IP-reuse decision. In: Hao Sh, Yanchun Zhang, Murk J. Bottema, Brian C. Lovel and Anthony J. Maeder, DICTA 2009 : 2009 digital image computing techniques and applications : proceedings. Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, VIC Australia, (66-73). 1 - 3 December 2009. doi:10.1109/DICTA.2009.21


Author Azman, A. W.
Bigdeli, A.
Biglari-Abhari, M.
Mustafah, Y. M.
Lovell, B. C.
Title of paper Exploiting Bayesian belief network for adaptive IP-reuse decision
Conference name Digital Image Computing: Techniques and Applications, DICTA 2009
Conference location Melbourne, VIC Australia
Conference dates 1 - 3 December 2009
Proceedings title DICTA 2009 : 2009 digital image computing techniques and applications : proceedings
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2009
Year available 2009
Sub-type Fully published paper
DOI 10.1109/DICTA.2009.21
Open Access Status
ISBN 9780769538662
142445297X
9781424452972
Editor Hao Sh
Yanchun Zhang
Murk J. Bottema
Brian C. Lovel
Anthony J. Maeder
Start page 66
End page 73
Total pages 8
Language eng
Abstract/Summary 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.
Subjects 1703 Computational Theory and Mathematics
1704 Computer Graphics and Computer-Aided Design
1712 Software
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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