Genetic multiway partitioning

Author

Shahookar, K. ; Mazumder, P.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA

fYear

1995

fDate

4-7 Jan 1995

Firstpage

365

Lastpage

369

Abstract

This research investigates a new software tool for Genetic Partitioning. The Genetic Algorithm is used to perform the partitioning with a significant improvement in result quality. Furthermore, it can optimize a cost function with multiple objectives and constraints. Separate algorithms have been developed, fine-tuned for bipartitioning and multiway partitioning. The bipartitioning problem is represented as a binary chromosome. Efficient bit-mask operations perform crossover, mutation, and net cut evaluation 32 bits at a time, without unpacking. The multiway partitioning algorithm has a global view of the problem, and generates/optimizes all the necessary partitions simultaneously. The algorithms were tested on the MCNC benchmark circuits, and the cut size obtained was lower than that for the conventional Fiduccia-Mattheyses algorithm

Keywords

VLSI; cellular arrays; circuit CAD; circuit optimisation; genetic algorithms; logic CAD; logic partitioning; software tools; CAD; MCNC benchmark circuits; VLSI; binary chromosome; bipartitioning; bit-mask operations; cost function; crossover; cut size; genetic multiway partitioning; multiple objectives; mutation; net cut evaluation; result quality; software tool; Benchmark testing; Biological cells; Circuit testing; Constraint optimization; Cost function; Genetic algorithms; Genetic mutations; Partitioning algorithms; Performance evaluation; Software tools;

fLanguage

English

Publisher

ieee

Conference_Titel

VLSI Design, 1995., Proceedings of the 8th International Conference on

Conference_Location

New Delhi

ISSN

1063-9667

Print_ISBN

0-8186-6905-5

Type

conf

DOI

10.1109/ICVD.1995.512140

Filename

512140