A new adaptation of particle swarm optimization applied to modern FPGA placement

This work presents a new adaptation of the discrete particle swarm optimization method applied to the FPGA placement problem, a crucial and time-consuming step in the FPGA synthesis flow. We evaluate the performance of the new optimizer against the existing version by embedding them into a publicly available FPGA placer Liquid to replace the simulated annealing-based optimizer used for the hard block optimization. The benchmark testing using Titan23 circuits shows the runtime efficiency of the new optimizer with comparable post-routed results as those of Liquid using simulated annealing