EVOLUTIONARY HEURISTICS FOR MULTIOBJECTIVE VLSI NETLIST BI-PARTITIONING

EVOLUTIONARY HEURISTICS FOR MULTIOBJECTIVE VLSI NETLIST BI-PARTITIONING. The 6th Saudi Engineering Conference, KFUPM, Dhahran, December 2002.

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Abstract

The problem of partitioning appears in several areas ranging from VLSI, parallel programming, to molecular biology. The interest in finding an optimal partitioning especially in VLSI, and has been a hot issue in recent years. In VLSI circuit partitioning, the problem of obtaining a minimum cut was of prime importance. Furthermore, with current trends partitioning has become a multi-objective problem, where power, delay and area in addition to minimum cut, need to be optimized. In this paper we employ two iterative heuristics for the optimization of VLSI Netlist Bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs) and Tabu Search (TS) [sadiq et al., 1999] respectively. Fuzzy rules are incorporated in order to design a multiobjective cost function. Both the techniques are applied to ISCAS-85/89 benchmark circuits and experimental results are reported and compared.

Item Type: Article
Divisions: College Of Computer Sciences and Engineering > Computer Engineering Dept
Depositing User: Users 4447 not found.
Date Deposited: 31 May 2008 09:18
Last Modified: 01 Nov 2019 16:27
URI: http://eprints.kfupm.edu.sa/id/eprint/1598