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The performance study of two genetic algorithm approaches for VLSI macro-cell layout area optimization

Rahim, H. A. and Rahman, A. A. A. and Ahmad, R. B. and Ariffin, W. N. F. W. and Ahmad, M.I. (2008) The performance study of two genetic algorithm approaches for VLSI macro-cell layout area optimization. In: Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008. Institute of Electrical and Electronics Engineers, New York, pp. 207-212. ISBN 978-076953136-6

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Official URL: http://dx.doi.org/10.1109/AMS.2008.117

Abstract

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 macrocell 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 [1]. However, SSGA outperforms SGA where it achieves faster convergence rate and obtains more near optimum area.

Item Type:Book Section
Additional Information:ISBN: 978-076953136-6; 2nd Asia International Conference on Modelling and Simulation, AMS 2008; Kuala Lumpur; 13 May 2008 through 15 May 2008
Uncontrolled Keywords:alpha particle spectrometers, asset management, binary trees, bioelectric phenomena, cells, chlorine compounds, cytology, data structures, diesel engines, embedded systems, file organization, genetic algorithms, integrated circuits, microelectronics, optimization, particle spectrometers, trees (mathematics)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:12782
Deposited By: Liza Porijo
Deposited On:29 Jun 2011 07:53
Last Modified:29 Jun 2011 07:53

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