Connection-oriented net model and fuzzy clustering techniques for K-way circuit partitioning

In this paper, we firstly propose a k-way connection-oriented net model, chain net model, to generalize the cut analysis for k-way circuit partitioning and to reduce the complexity of edges for the representation of a multiple-pin net between the transformation of a hypergraph and an edge-weighted graph. Furthermore, based on the techniques of fuzzy c-means clustering, we develop and propose fuzzy c-means graph clustering to obtain k groups of fuzzy memberships for the vertices in the mapped graph according to the global information of all the net connections. Finally, by the area information of any cell in the circuit netlist, these k groups of fuzzy memberships will lead to a cut-driven or balance-driven k-way circuit partitioning. As a result, k-way circuit partitioning has been implemented for testing MCNC circuit benchmarks and the experimental results show that the proposed partitioning approach generates effective results on the partitioning cut and the partitioning balance for these benchmarks