A hierarchical circuit clustering algorithm with stable performance

Abstract

Clustering is almost essential in improving the performance of iterative partitioning algorithms. In this paper, we present a clustering algorithm based on the following observation: if a group of cells is assigned to the same partition in numerous local optimum solutions, it is desirable to merge the group into a cluster. The proposed algorithm finds such a group of cells from randomly generated local optimum solutions and merges it into a cluster. We implemented a multilevel bipartitioning algorithm (MBP) based on the proposed clustering algorithm. For MCNC benchmark netlists, MBP improves the total average cut size by 9% and the total best cut size by 3-4%, compared with the previous state-of-the-art partitioners.