A BBN-based framework for adaptive IP-reuse
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 con...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
ACM
2009
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/28264/ http://irep.iium.edu.my/28264/ http://irep.iium.edu.my/28264/1/A_BBN-based_Framework_for_Adaptive_IP-Reuse.pdf |
| Summary: | 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. |
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