A two-way partitioning algorithm is presented that significantly improves on the highly unstable results typically obtained from the traditional Kernighan-Lin-based algorithms. The algorithm groups highly connected components into clusters and rearranges the clusters into two final subsets with specified sizes. It is known that the grouping operations reduce the complexity, and thus improve the results, of partitioning very large circuits. However, if the grouping is inappropriate, the partitioning results may degenerate. To prevent degeneration, a ratio cut approach is used to do the grouping. By a series of experiments based on the tradeoff between cut weight and CPU time, the value which controls the resultant number of groups is determined. Good experimental results have been observed for cut weight and CPU time.>

Circuit partitioning for hierarchical VLSI design is addressed. A partitioning approach called ratio cut is proposed. It is demonstrated that the ratio cut algorithm can locate the clustering structures in the circuit. Finding the optimal ratio cut is NP-complete. However, in certain cases the ratio cut can be solved by linear programming techniques via the multicommodity flow formulation. Also proposed is a fast heuristic algorithm running in linear time with respect to the number of pins in the circuit. Experiments show good results in all tested cases.>

A two-way partitioning algorithm is presented which significantly improves on the highly unstable results from the traditional Kernighan-Lin based algorithms. The algorithm groups strongly connected components into clusters, and rearranges the clusters into two final subsets with specified sizes. It is known that the grouping operations reduce the complexity and thus improve the results of partitioning very large circuits. However, if the grouping is inappropriate, the partitioning results may degenerate. To prevent degeneration, the authors use a ratio cut approach to do the grouping. By a series of experiments based on the tradeoff between cut capacity and CPU time, the authors determine an optimal value to control the resultant number of groups. Good experimental results have been observed in terms of cut capacity and CPU time.>

A partitioning approach called ratio cut is proposed. The authors demonstrate that the ratio cut algorithm can locate the clustering structures in the circuit. Finding the optimal ratio cut is NP-complete. However, in certain cases the ratio cut can be solved by linear programming techniques via the multicommodity flow problem. They also propose a fast heuristic algorithm running in linear time with respect to the number of pins in the circuit. Experiments show good results in all tested cases, and as much as 70% improvement over the Kernighan-Lin algorithm in terms of the proposed ratio metric.>