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...
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iium-282642013-09-17T09:14:49Z http://irep.iium.edu.my/28264/ A BBN-based framework for adaptive IP-reuse Azman, Amelia Wong Bigdeli, Abbas Biglari-Abhari, Morteza Mohd Mustafah, Yasir Lovell, Brian T Technology (General) 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. ACM 2009 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/28264/1/A_BBN-based_Framework_for_Adaptive_IP-Reuse.pdf Azman, Amelia Wong and Bigdeli, Abbas and Biglari-Abhari, Morteza and Mohd Mustafah, Yasir and Lovell, Brian (2009) A BBN-based framework for adaptive IP-reuse. In: Proceedings of the 6th FPGAworld Conference, 9 Sept. 2009, Kista, Stockholm, Sweden. http://doi.acm.org/10.1145/1667520.1667521 |
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T Technology (General) |
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T Technology (General) Azman, Amelia Wong Bigdeli, Abbas Biglari-Abhari, Morteza Mohd Mustafah, Yasir Lovell, Brian A BBN-based framework for adaptive IP-reuse |
description |
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. |
format |
Conference or Workshop Item |
author |
Azman, Amelia Wong Bigdeli, Abbas Biglari-Abhari, Morteza Mohd Mustafah, Yasir Lovell, Brian |
author_facet |
Azman, Amelia Wong Bigdeli, Abbas Biglari-Abhari, Morteza Mohd Mustafah, Yasir Lovell, Brian |
author_sort |
Azman, Amelia Wong |
title |
A BBN-based framework for adaptive IP-reuse |
title_short |
A BBN-based framework for adaptive IP-reuse |
title_full |
A BBN-based framework for adaptive IP-reuse |
title_fullStr |
A BBN-based framework for adaptive IP-reuse |
title_full_unstemmed |
A BBN-based framework for adaptive IP-reuse |
title_sort |
bbn-based framework for adaptive ip-reuse |
publisher |
ACM |
publishDate |
2009 |
url |
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 |
first_indexed |
2023-09-18T20:41:45Z |
last_indexed |
2023-09-18T20:41:45Z |
_version_ |
1777409411816357888 |