A BBN-based framework for adaptive IP-reuse

Authors:
Amelia W. Azman;Abbas Bigdeli;Morteza Biglari-Abhari;Yasir M. Mustafah;Brian C. Lovell
Affiliations:
The University of Queensland and National ICT Australia, Australia;The University of Queensland, Australia;The University of Auckland, New Zealand;The University of Queensland and National ICT Australia, Australia;The University of Queensland and National ICT Australia, Australia
Venue:
Proceedings of the 6th FPGAworld Conference
Year:
2009

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Abstract

The complexity of implementing vision algorithm on embedded systems can greatly benefit from research in HW/SW partitioning and IP-reuse. This paper presents a novel research work of a hybrid HW/SW partitioning method that combines heuristic and knowledge-based approaches to satisfy user-defined constraints. In order to achieve this objective, Bayesian Belief Network (BBN) is utilised and incorporated into the framework to produce a reliable HW/SW partitioning for a given vision algorithm. To provide a better convergence, software weight is incorporated into the link matrices. The outcome of the framework will be the partitioned modules that satisfy the user-defined timing and resource constraints. In this paper, we also report on comparison of our proposed framework with the previous work reported in the literature including: BBN by University of Arizona, the exhaustive algorithm and the greedy algorithm.