A probabilistic multicommodity-flow solution to circuit clustering problems

Circuit clustering, which plays a fundamental role in hierarchical designs, is discussed. Identifying strongly connected components in the circuits can significantly reduce the complexity of the design and improve the performance of the design process. However, there has not been a clear objective function for circuit clustering. A clustering metric based on the random graph model and the ratio cust concept is presented. A probabilistic, multicommodity flow based algorithm is proposed and tested under the clustering metric. Experimental results show that this algorithm generates promising results with respect to the proposed metric. Extensions and directions for future work are also proposed.<>