The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization

Link to publisher's homepage at http://ieeexplore.ieee.org

Saved in:
Bibliographic Details
Main Authors: Hasliza, A. Rahim@Samsuddin, Rahman, A. A A, R. Badlishah, Ahmad, Wan Nur Suryani Firuz, Wan Ariffin, Muhammad Imran, Ahmad
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineering (IEEE) 2009
Subjects:
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/6885
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-6885
record_format dspace
spelling my.unimap-68852017-11-29T04:45:08Z The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization Hasliza, A. Rahim@Samsuddin Rahman, A. A A R. Badlishah, Ahmad Wan Nur Suryani Firuz, Wan Ariffin Muhammad Imran, Ahmad Integrated circuit layout Genetic algorithms Logic design Trees (mathematics) Very large scale integrated (VLSI) Circuit optimisation Integrated circuits Integrated circuits -- Design and construction Link to publisher's homepage at http://ieeexplore.ieee.org Very large scale integrated (VLSI) design has been the subject of much research since the early 1980s where the VLSI cell placement emerges to be a crucial stage in the chip design. Its area optimization is very important in order to reduce the delay and include more functionalities to the designed chip. The VLSI cell area optimization continues to become increasingly important to the performance of VLSI design due to the accelerating of the design complexities in VLSI. Thus, this paper addresses the performance comparisons of two different types of genetic algorithm (GA) techniques for VLSI macro-cell layout area optimization by utilizing the adopted method of cell placement that is binary tree method. Two GA approaches which are simple genetic algorithm (SGA) and steady-state genetic algorithm (SSGA) have been implemented and their performances in converging to their global minimums are examined and discussed. The performances of these techniques are tested on Microelectronics Center of North Carolina (MCNC) benchmark circuit's data set. The experimental results demonstrate that both algorithms achieve acceptable area requirement compared to the slicing floorplan approach (Lin et al., 2002). However, SSGA outperforms SGA where it achieves faster convergence rate and obtains more near optimum area. 2009-08-13T07:43:52Z 2009-08-13T07:43:52Z 2008 Article p.207-212 978-0-7695-3136-6 http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4530477 http://hdl.handle.net/123456789/6885 en Proceedings of the 2nd Asia International Conference on Modeling & Simulation (AICMS 08) Institute of Electrical and Electronics Engineering (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Integrated circuit layout
Genetic algorithms
Logic design
Trees (mathematics)
Very large scale integrated (VLSI)
Circuit optimisation
Integrated circuits
Integrated circuits -- Design and construction
spellingShingle Integrated circuit layout
Genetic algorithms
Logic design
Trees (mathematics)
Very large scale integrated (VLSI)
Circuit optimisation
Integrated circuits
Integrated circuits -- Design and construction
Hasliza, A. Rahim@Samsuddin
Rahman, A. A A
R. Badlishah, Ahmad
Wan Nur Suryani Firuz, Wan Ariffin
Muhammad Imran, Ahmad
The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization
description Link to publisher's homepage at http://ieeexplore.ieee.org
format Article
author Hasliza, A. Rahim@Samsuddin
Rahman, A. A A
R. Badlishah, Ahmad
Wan Nur Suryani Firuz, Wan Ariffin
Muhammad Imran, Ahmad
author_facet Hasliza, A. Rahim@Samsuddin
Rahman, A. A A
R. Badlishah, Ahmad
Wan Nur Suryani Firuz, Wan Ariffin
Muhammad Imran, Ahmad
author_sort Hasliza, A. Rahim@Samsuddin
title The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization
title_short The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization
title_full The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization
title_fullStr The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization
title_full_unstemmed The performance study of two genetic algorithm approaches for VLSI Macro-Cell layout area optimization
title_sort performance study of two genetic algorithm approaches for vlsi macro-cell layout area optimization
publisher Institute of Electrical and Electronics Engineering (IEEE)
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/6885
_version_ 1643802700165414912
score 13.222552