FPGA Placement Using Genetic Algorithm with Simulated Annealing

Abstract

A mixed genetic algorithm and simulated annealing (GASA) algorithm is used for the placement of symmetrical FPGA. The proposed algorithm includes 2 stage processes. In the first stage process it optimizes placement solutions globally using GA. In the second stage process it locally improves solution. GASA overcomes the slow convergence of genetic algorithm in the late phase of the process of genetic algorithm. The results show that GASA consumes less CPU time than GA and could achieve performance as good as versatile placement and routing tool in terms of placement cost.