A genetic algorithm for VLSI floorplanning using O-Tree representation
Floorplanning is one of the most important problems in VLSI physical design automation. A fundamental research problem in the VLSI floorplanning is representation because it determines the size of search space and the complexity of the transformation between a representation and its corresponding floorplan. O-tree representation is one of the most efficient floorplan representations as it has the smallest search space among all the admissible floorplan representations and the computational complexity of transformation between a representation and its corresponding floorplan is only O(n). The efficiency of O-tree representation was demonstrated by a deterministic algorithm proposed by Guo et al.. The deterministic algorithm can quickly find a reasonably good floorplan. However, the deterministic floorplanning algorithm, by its nature, is a local search algorithm, and thereby may not be able to find an optimal or near-optimal solution sometimes. This paper presents a genetic algorithm for the VLSI floorplanning problem using O-tree representation. Experimental results show that the GA can consistently produce better results than the deterministic algorithm.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Additional Information:||For more information, please refer to the publisher's website (see hypertext link) or contact the author. Author contact details: email@example.com|
|Keywords:||floorplanning, genetic algorithm, VLSI, ordered tree|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)|
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Optimisation (010303)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Microelectronics and Integrated Circuits (090604)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2005 Springer|
|Copyright Statement:||Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springer.de/comp/lncs/ Lecture Notes in Computer Science|
|Deposited On:||17 Jul 2007|
|Last Modified:||29 Feb 2012 23:14|
Repository Staff Only: item control page