Cloud Computing for VLSI Floorplanning Considering Peak Temperature Reduction

Abstract

Cloud computing has recently emerged as a promising computing paradigm, which offers unprecedented computing power and flexibility in the distributed computing environment. Despite the trend that electronic design automation industry has prepared to embrace the cloud concept, there is still no research publication on designing VLSI floorplanning algorithms for a cloud computing platform. This paper proposes the first such algorithm for thermal driven floorplanning. Since the existing floorplanning techniques are based on simulated annealing that are sequential algorithms and difficult to parallelize, a new thermal driven floorplanning algorithm is proposed, which can be easily parallelized in a cloud computing environment. This algorithm uses an advanced adjacency probability cross entropy optimization and a new integer linear programming-based resources provisioning to efficiently use the heterogeneous computation resources and handle the uncertainty of machine waiting time in a cloud. The experimental results on the standard GSRC benchmark circuits demonstrate that the proposed algorithm can significantly reduce the peak temperature (up to 24 °) compared with the simulated annealing technique. In the simulated cloud computing environment, it runs over 30% faster than the simulated annealing technique with moderate overhead in monetary expense due to the fact that the proposed algorithm is parallelization friendly. Further, our algorithm can effectively compute the scheduling solutions considering the uncertainty in waiting time.