Fuzzy simulated evolution for power and performance optimization ofVLSI placement

In this paper, an algorithm for VLSI standard cell placement for low power and high performance design is presented. This is a hard multiobjective combinatorial optimization problem with no known exact and efficient algorithm that can guarantee finding a solution of specific or desirable quality. Ap...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Sait, Sadiq M. (author)
مؤلفون آخرون: Youssef, H. (author), Khan, J.A. (author), El-Maleh, A. (author), unknown (author)
التنسيق: article
منشور في: 2001
الموضوعات:
الوصول للمادة أونلاين:https://eprints.kfupm.edu.sa/id/eprint/14196/1/14196_1.pdf
https://eprints.kfupm.edu.sa/id/eprint/14196/2/14196_2.doc
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author Sait, Sadiq M.
author2 Youssef, H.
Khan, J.A.
El-Maleh, A.
unknown
author2_role author
author
author
author
author_facet Sait, Sadiq M.
Youssef, H.
Khan, J.A.
El-Maleh, A.
unknown
author_role author
dc.creator.none.fl_str_mv Sait, Sadiq M.
Youssef, H.
Khan, J.A.
El-Maleh, A.
unknown
dc.date.none.fl_str_mv 2001
2020
dc.format.none.fl_str_mv application/pdf
application/msword
dc.identifier.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/14196/1/14196_1.pdf
https://eprints.kfupm.edu.sa/id/eprint/14196/2/14196_2.doc
(2001) Fuzzy simulated evolution for power and performance optimization ofVLSI placement. Neural Networks, 2001. Proceedings. IJCNN '01. International Joint conference, 1.
dc.language.none.fl_str_mv en
en
dc.publisher.none.fl_str_mv IEEE
dc.relation.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/14196/
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Computer
dc.title.none.fl_str_mv Fuzzy simulated evolution for power and performance optimization ofVLSI placement
dc.type.none.fl_str_mv Article
PeerReviewed
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description In this paper, an algorithm for VLSI standard cell placement for low power and high performance design is presented. This is a hard multiobjective combinatorial optimization problem with no known exact and efficient algorithm that can guarantee finding a solution of specific or desirable quality. Approximation iterative heuristics such as simulated evolution (SE) are best suited to perform an intelligent search of the solution space. SE comprises three steps, evaluation, selection and allocation. Due to imprecise nature of design information at the placement stage, the various objectives and constraints are expressed in fuzzy domain. The search is made to evolve towards a vector of fuzzy goals. In this work, a new method to calculate membership in evaluation stage is proposed. Selection stage is also fuzzified and a new controlled fuzzy operator is introduced. The proposed heuristics is compared with genetic algorithm (GA) and the proposed fuzzy operator is compared with fuzzy ordered weighted averaging operator (OWA). Fuzzified SE (FSE) with controlled fuzzy operators was able to achieve better solutions
eu_rights_str_mv openAccess
format article
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identifier_str_mv (2001) Fuzzy simulated evolution for power and performance optimization ofVLSI placement. Neural Networks, 2001. Proceedings. IJCNN '01. International Joint conference, 1.
language_invalid_str_mv en
network_acronym_str KFUPM
network_name_str King Fahd University of Petroleum and Minerals
oai_identifier_str oai::14196
publishDate 2001
publisher.none.fl_str_mv IEEE
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spelling Fuzzy simulated evolution for power and performance optimization ofVLSI placementSait, Sadiq M.Youssef, H.Khan, J.A.El-Maleh, A.unknownComputerIn this paper, an algorithm for VLSI standard cell placement for low power and high performance design is presented. This is a hard multiobjective combinatorial optimization problem with no known exact and efficient algorithm that can guarantee finding a solution of specific or desirable quality. Approximation iterative heuristics such as simulated evolution (SE) are best suited to perform an intelligent search of the solution space. SE comprises three steps, evaluation, selection and allocation. Due to imprecise nature of design information at the placement stage, the various objectives and constraints are expressed in fuzzy domain. The search is made to evolve towards a vector of fuzzy goals. In this work, a new method to calculate membership in evaluation stage is proposed. Selection stage is also fuzzified and a new controlled fuzzy operator is introduced. The proposed heuristics is compared with genetic algorithm (GA) and the proposed fuzzy operator is compared with fuzzy ordered weighted averaging operator (OWA). Fuzzified SE (FSE) with controlled fuzzy operators was able to achieve better solutionsIEEE20012020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/mswordhttps://eprints.kfupm.edu.sa/id/eprint/14196/1/14196_1.pdfhttps://eprints.kfupm.edu.sa/id/eprint/14196/2/14196_2.doc (2001) Fuzzy simulated evolution for power and performance optimization ofVLSI placement. Neural Networks, 2001. Proceedings. IJCNN '01. International Joint conference, 1. enenhttps://eprints.kfupm.edu.sa/id/eprint/14196/info:eu-repo/semantics/openAccessoai::141962019-11-01T14:04:40Z
spellingShingle Fuzzy simulated evolution for power and performance optimization ofVLSI placement
Sait, Sadiq M.
Computer
status_str publishedVersion
title Fuzzy simulated evolution for power and performance optimization ofVLSI placement
title_full Fuzzy simulated evolution for power and performance optimization ofVLSI placement
title_fullStr Fuzzy simulated evolution for power and performance optimization ofVLSI placement
title_full_unstemmed Fuzzy simulated evolution for power and performance optimization ofVLSI placement
title_short Fuzzy simulated evolution for power and performance optimization ofVLSI placement
title_sort Fuzzy simulated evolution for power and performance optimization ofVLSI placement
topic Computer
url https://eprints.kfupm.edu.sa/id/eprint/14196/1/14196_1.pdf
https://eprints.kfupm.edu.sa/id/eprint/14196/2/14196_2.doc