A genetic algorithm for VLSI floorplanning using o-tree representation

Authors:
Maolin Tang;Alvin Sebastian
Affiliations:
School of Software Engineering and Data Communications, Queensland University of Technology, Brisbane, Australia;School of Software Engineering and Data Communications, Queensland University of Technology, Brisbane, Australia
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

Citing 5
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Automatic floorplan design

DAC '82 Proceedings of the 19th Design Automation Conference
VLSI placement and area optimization using a genetic algorithm tobreed normalized postfix expressions

IEEE Transactions on Evolutionary Computation
GALLO: a genetic algorithm for floorplan area optimization

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Floorplanning using a tree representation

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.