A probability-based approach to VLSI circuit partitioning

Abstract

Abstract-Iterative-improvement two-way min-cut partitioning is an important phase in most circuit placement tools, and finds use in many other computer-aided design (CAD) applications. Most iterative improvement techniques for circuit netlists like the Fiduccia-Mattheyses (FM) method compute the gains of nodes using local netlist information that is only concerned with the immediate improvement in the cutset. This can lead to misleading gain information. Krishnamurthy suggested a lookahead (LA) gain calculation method to ameliorate this situation; however, as we show, it leaves room for improvement. We present here a probabilistic gain computation approach called probabilistic partitioner (PROP) that is capable of capturing the future implications of moving a node at the current time. We also propose an extended algorithm SHRINK-PROP that increases the probability of removing recently "perturbed" nets (nets whose nodes have been moved for the first time) from the cutset. Experimental results on medium-to large-size ACM/SIGDA benchmark circuits show that PROP and SHRINK-PROP outperform previous iterative-improvement methods like FM (by about 30% and 37%, respectively) and LA (by about 27% and 34%, respectively). Both PROP and SHRINK-PROP also obtain much better cutsizes than many recent state-of-the-art partitioners like EIG1, WINDOW, MELO, PARABOLI, GFM and GMetis (by 4.5% to 67%). Our empirical timing results reveal that PROP is appreciably faster than most recent techniques. We also obtain results on the more recent ISPD-98 benchmark suite that show similar substantial mincut improvements by PROP and SHRINK-PROP over FM (24% and 31%, respectively). It is also noteworthy that SHRINK-PROP's results are within 2.5% of those obtained by hMetis, one of the best multilevel partitioners. However, the multilevel paradigm is orthogonal to SHRINK-PROP. Further, since it is a "flat" partitioner, it has advantages over hMetis in partition-driven placement applications.Index Terms-Clustering effect, iterative-improvement, min-cut partitioning, probabilistic gain, VLSI circuit.