Evolutionary Algorithms for VLSIMultiobjective Netlist Partitioning

(2006) Evolutionary Algorithms for VLSIMultiobjective Netlist Partitioning. ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS 13 (1): 15-21 MAR 2005.


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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 partition especially in VLSI has been a hot issue in recent years. In VLSI circuit partitioning, the problem of obtaining a minimum cut is of prime importance. With current trends, partitioning with multiple objectives which includes power, delay and area, in addition to minimum cut is in vogue. In this paper, we engineer three iterative heuristics for the optimization of VLSI netlist bi-Partitioning. These heuristics are based on Genetic Algorithms (GAs), Tabu Search (TS) and Simulated Evolution (SimE). Fuzzy rules are incorporated in order to handle the multiobjective cost function. For SimE, fuzzy goodness functions are designed for delay and power, and proved efficient. A series of experiments are performed to evaluate the efficiency of the algorithms. ISCAS-85/89 benchmark circuits are used and experimental results are reported and analyzed to compare the performance of GA, TS and SimE. Further, we compared the results of the iterative heuristics with a modified FM algorithm, named PowerFM, which targets power optimization. PowerFM performs better in terms of power dissipation for smaller circuits. For larger sized circuits SimE outperforms PowerFM in terms of all three, delay, number of net cuts, and power dissipation. Keywords: Genetic Algorithms, Tabu Search, Simulated Evolution, multiobjective, Fuzzy Logic, Netlist partitioning.

Item Type: Article
Subjects: Computer
Department: College of Computing and Mathematics > Computer Engineering
Depositing User: AbdulRahman
Date Deposited: 09 Mar 2008 11:51
Last Modified: 01 Nov 2019 13:23
URI: http://eprints.kfupm.edu.sa/id/eprint/259