k-Way Partitioning Algorithm Based on Re-Clustering and Discrete Optimization

To achieve a better partitioning of VLSI circuit, re-clustering and discrete optimization are applied to the k-way partitioning algorithm. Firstly, re-clustering is used to reduce the scale of hypergraph, i.e., the rating function value between two vertices is calculated according to the given partitionings, and vertices are clustered according to the magnitude of the rating function values. Secondly, the hypergraph is converted to a star graph, and the k-way partitioning problem is transformed to an unconstrained discrete optimization problem. In turn, an algorithm is designed to iteratively move the vertices with the largest gain. During the solution process, the balancing constraints are relaxed, allowing a solution to be temporarily in the infeasible region, which expands the solution space of the problem. The proposed algorithm, hMETIS-Kway and KaHyPar-K are tested on the same platform on the ISPD98 test benchmarks, and the min-cut and running time are compared. Experimental results show that, the proposed algorithm is superior to hMETIS-Kway, especially when k=2, for which the min-cut is reduced by 0.173 and the runtime is sped up by 0.706. The proposed algorithm has almost the same improvement effect over KaHyPar-K. © 2024 Institute of Computing Technology. All rights reserved.