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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.

References
  1. Anand, S. Saravanasankar, S. Subbaraj, P. 2012. Customized simulated annealing based decision algorithms for combinatorial optimization in VLSI floorplanning problem. Comput Optim Appl pp. 667–689.
  2. Bernard, C. 1983 The bottom-left bin packing heuristic: An efficient implementation. IEEE Trans. Comput. , vol. 32, no. 8, pp. 697–707.
  3. Chang, Y-C. , Chang, Y. W. , Chang G. -M. Wu, and Wu, S. -W. , 2000. B*-trees: a new representation for non-slicing floorplans. In Proceedings of the ACM/IEEE Design Automation Conf. , pp. 458–463.
  4. Chen Jianli, Zhu Wenxing and Ali M. M 2011. A hybrid simulated annealing algorithm for non-slicing VLSI floorplanning. IEEE Trans VLSI Syst Man Cybern C 41(4), pp. 544-553.
  5. Guolong Chen, Wenzhong Guo, Yuzhong Chen. 2010. A PSO based intelligent decision algorithm for VLSI floorplanning. Springer, Soft Comput 14, pp. 1329-1337.
  6. Guo, P. N. Cheng, C. K. Yoshimura, T. 1999. An O-tree representation of non-slicing floorplan and its applications. In: Proceedings of the 36th ACM/IEEE conference on design automation conference, New Orleans, Louisiana, USA, pp 268–273.
  7. Huang, W. Chen, D. and Xu, R. 2007. A new heuristic algorithm for rectangle packing. Computers Oper. Res. , vol. 34, no. 11, pp. 3270–3280.
  8. Hung, W-L. Xie, Y. Vijaykrishnan, N. Addo-Quaye, C. Theocharides,T. and Irwin, M. J. 2005. Thermal-Aware Floorplanning Using Genetic Algorithms. IEEE In International Symposium on Quality Electronic Design (ISQED), pp. 7695-7701.
  9. Murata, H. Fujiyoshi, K. . Nakatake, S. and Kajatani, Y. 1995. Rectangular-Packing-Based Module Placement ICCAD, pp. 472-479.
  10. Nakatake, S. Fujiyoshi, K. Murata, H. and Kajitani, Y. 1996. Module Placement on BSG-Structure and IC Layout Applications. ICCAD, pp. 484-491.
  11. Sivanandan, S. N. and. Deepa, S. N. 2008. Book on Introduction to genetic algorithms. Springer-Verlag Berlin Heidelberg.
  12. Sun, T. Y. Hsieh, S. T. Wang, H. M. Lin, C. W. 2006. Floorplanning based on particle swarm optimization. IEEE computer society annual symposium on VLSI, Karlsruhe, Germany. IEEE,pp 7–11.
  13. Valenzuela, C. and Wang, P. 2002. VLSI placement and area optimization using a genetic algorithm to breed normalized postfix expressions. IEEE Trans. Evol. Comput. , vol. 6, no. 4, pp. 390–401.
  14. Wong, D. F. and Tang, X. 2001. FAST-SP: A fast algorithm for block placement based on sequence pair. In Proc. Asia and South Pacific Design Automation Conf. , pp. 521–526.
  15. The MCNC Benchmark Problems for VLSI Floorplanning. [Online]. Available: http://www. mcnc. org.
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)