Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application

Simulated annealing has been one of the most popular stochastic optimization methods used in the VLSI CAD field in the past two decades. Recently, a new Monte Carlo and optimization method, named dynamic weighting Monte Carlo [WL97], has been introduced and successfully applied to the traveling salesman problem, neural network training [WL97], and spin-glasses simulation [LW99]. In this paper, we have successfully applied dynamic weighting Monte Carlo algorithm to the constrained floorplan design with consideration of both area and wirelength minimization. Our application scenario is the constrained floorplan design for mixed signal MCMs, where we need to place all the analog modules together in groups so that they can share common power and ground and are separate from those used by the digital modules. Our experiments indicate that the dynamic weighting Monte Carlo algorithm is very effective for constrained floorplan optimization. It outperforms the simulated annealing for a real mixed signal MCM design by 19.5% in wirelength, with slight area improvement. This is the first work adopting the dynamic weighting Monte Carlo optimization method for solving VLSI CAD problems. We believe that this method has applications to many other VLSI CAD optimization problems.