A novel placement algorithm for symmetrical FPGA

Placement becomes a vital current concern in FPGA CAD flow. This paper presents a novel FPGA placement algorithm based on ant colony optimization (ACO), a new meta-heuristic algorithm characterized by inherent parallelism, positive feedback mechanism, and stochastic decision policy with swarm intelligence. We test the performance of our proposed algorithm using a set of Microelectronics Center of North Carolina (MCNC) benchmark circuits on island-style architecture FPGA, and have a comprehensive comparison with simulated annealing (SA), genetic algorithm (GA) and hybrid meta-heuristic approach mixed GA and SA. The experimental results show that our placement algorithm can achieves promising performance and is a potential approach for FPGA placement.