Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/27186
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dc.coverage.spatialCertain optimization techniques for Floorplanning in vlsi physical designen_US
dc.date.accessioned2014-10-27T05:52:21Z-
dc.date.available2014-10-27T05:52:21Z-
dc.date.issued2014-10-27-
dc.identifier.urihttp://hdl.handle.net/10603/27186-
dc.description.abstractnewlineFloorplanning is an important stage in VLSI Very Large Scale newlineIntegrated Circuits physical design cycle as it determines the performance newlinesize and reliability of VLSI chips Given a set of circuit components or newline modules and a netlist specifying interconnections between the modules the newlinegoal of VLSI floorplanning is to find a floorplan for the modules such that no newlinemodule overlaps with another and the area of the floorplan and the newlineinterconnection between the modules are minimized The representation of newlinefloorplans determines the size of the search space and the complexity of newlinetransformation between a representation and its corresponding floorplan newlineThe objective of this thesis work entails evaluating the use of newlineParticle Swarm Optimization PSO to produce solutions for the VLSI newlinefloorplanning problems This work also demonstrates the successful newlineapplication of Differential Evolution DE an improved version of Genetic newlineAlgorithm GA to the VLSI floorplanning problems The important newlineadvantages of DE are its simplicity fast convergence and easy for newlineimplementation Sequence Pair SP representation with PSO HPSO and DE newlinealgorithms for VLSI floorplanning are proposed Hybrid Particle Swarm newlineOptimization HPSO utilizes the basic mechanism of PSO and the natural newlineselection mechanism which is usually utilized by GA newline newlineen_US
dc.format.extentxix, 156p.en_US
dc.languageEnglishen_US
dc.relationp144-154.en_US
dc.rightsuniversityen_US
dc.titleCertain optimization techniques for Floorplanning in vlsi physical designen_US
dc.title.alternativeen_US
dc.creator.researcherJackuline moni Den_US
dc.subject.keywordDifferential Evolutionen_US
dc.subject.keywordHybrid Particle Swarm Optimizationen_US
dc.subject.keywordParticle Swarm Optimizationen_US
dc.subject.keywordVery Large Scale Integrateden_US
dc.description.notereference p144-154.en_US
dc.contributor.guideArumugam Sen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US
dc.date.registeredn.d.en_US
dc.date.completed01/06/2009en_US
dc.date.awarded30/06/2009en_US
dc.format.dimensions23cm.en_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificate.pdf5.58 kBAdobe PDFView/Open
03_abstract.pdf9.85 kBAdobe PDFView/Open
04_acknowledgement.pdf6.74 kBAdobe PDFView/Open
05_content.pdf37.98 kBAdobe PDFView/Open
06_chapter1.pdf48.94 kBAdobe PDFView/Open
07_chapter2.pdf109.8 kBAdobe PDFView/Open
08_chapter3.pdf411.04 kBAdobe PDFView/Open
09_chapter4.pdf305.6 kBAdobe PDFView/Open
10_chapter5.pdf307.88 kBAdobe PDFView/Open
11_chapter6.pdf120.33 kBAdobe PDFView/Open
12_chapter7.pdf59.99 kBAdobe PDFView/Open
13_reference.pdf44.3 kBAdobe PDFView/Open
14_publication.pdf5.74 kBAdobe PDFView/Open
15_vitae.pdf5.8 kBAdobe PDFView/Open


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