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Exploiting Bayesian belief network for adaptive IP-reuse decision

Azman, Amelia Wong and Bigdeli, Abbas and Biglari-Abhari, Morteza and Mohd Mustafah, Yasir and Lovell, Brian (2009) Exploiting Bayesian belief network for adaptive IP-reuse decision. In: Digital Image Computing: Techniques and Applications 2009 (DICTA '09), 1 - 3 December 2009, Melbourne, VIC.

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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.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 5107/28297 (ISBN: 978-1-4244-5297-2)
Uncontrolled Keywords: Adaptive systems;Application software;Artificial intelligence;Artificial neural networks;Australia;Bayesian methods;Digital images;Field programmable gate arrays;Hardware;Smart cameras;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;bayesian belief network;codesign;partitioning;
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Dr Yasir Mohd Mustafah
Date Deposited: 18 Sep 2013 09:58
Last Modified: 18 Sep 2013 10:44
URI: http://irep.iium.edu.my/id/eprint/28297

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