A PSO-based intelligent decision algorithm for VLSI floorplanning

Abstract

Floorplanning is an important issue in the very large-scale integrated (VLSI) circuit design automation as it determines the performance, size, yield and reliability of VLSI chips. This paper proposes a novel intelligent decision algorithm based on the particle swarm optimization (PSO) technique to obtain a feasible floorplanning in VLSI circuit physical placement. The PSO was applied with integer coding based on module number and a new recommended value of acceleration coefficients for optimal placement solution. Inspired by the physics of genetic algorithm (GA), the principles of mutation and crossover operator in GA are incorporated into the proposed PSO algorithm to make this algorithm to break away from local optima and achieve a better diversity. Experiments employing MCNC and GSRC benchmarks show that the proposed algorithm is effective. The proposed algorithm can avoid local minimum and performs well in convergence. The experimental results of the proposed method in this paper can also greatly help floorplanning decision making in VLSI circuit design automation.