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Reseach Article

Performance Analysis of VLSI Floor planning using Evolutionary Algorithm

Published on December 2013 by P. Sivaranjani, K. K. Kawya
International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
Foundation of Computer Science USA
ICIIIOES - Number 9
December 2013
Authors: P. Sivaranjani, K. K. Kawya
2cca5bbc-4c24-45cf-8503-fb73a57bb273

P. Sivaranjani, K. K. Kawya . Performance Analysis of VLSI Floor planning using Evolutionary Algorithm. International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences. ICIIIOES, 9 (December 2013), 42-46.

@article{
author = { P. Sivaranjani, K. K. Kawya },
title = { Performance Analysis of VLSI Floor planning using Evolutionary Algorithm },
journal = { International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences },
issue_date = { December 2013 },
volume = { ICIIIOES },
number = { 9 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 42-46 },
numpages = 5,
url = { /proceedings/iciiioes/number9/14348-1662/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
%A P. Sivaranjani
%A K. K. Kawya
%T Performance Analysis of VLSI Floor planning using Evolutionary Algorithm
%J International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences
%@ 0975-8887
%V ICIIIOES
%N 9
%P 42-46
%D 2013
%I International Journal of Computer Applications
Abstract

Floorplanning is an important physical design step for hierarchical, building-block design methodology. When the circuit size get increases the complexity of the circuit also increases. To deal with the increasing design complexity the intellectual property (IP) modules are mostly used in floorplanning. This paper presents a Hybrid particle swarm optimization algorithm for floorplanning optimization. Here B*tree is used at the initial stage in order to avoid overlapping of modules and later, PSO algorithm along with the concept of crossover and mutation from Genetic algorithm is used to get optimal placement solution. The main objective of floorplanning is to minimize the chip area and interconnection wire length. The Experimental results on Microelectronic Center of North Carolina (MCNC) benchmark circuits shows that our algorithm performs better convergence than the other methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Hybrid Particle Swarm Optimization (hpso) Genetic Algorithm (ga) Crossover Mutation Microelectronic Center Of North Carolina (mcnc) Very Large Scale Integrated Circuits(vlsi)