Circuit clustering using graph coloring
ISPD '99 Proceedings of the 1999 international symposium on Physical design
Efficient circuit clustering for area and power reduction in FPGAs
FPGA '02 Proceedings of the 2002 ACM/SIGDA tenth international symposium on Field-programmable gate arrays
Learning evaluation functions to improve optimization by local search
The Journal of Machine Learning Research
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An adaptive multi-start graph partitioning algorithm for structuring cellular networks
Journal of Heuristics
Seeking global edges for traveling salesman problem in multi-start search
Journal of Global Optimization
Analysis of Heuristic Graph Partitioning Methods for the Assignment of Packet Control Units in GERAN
Wireless Personal Communications: An International Journal
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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VLSI netlist partitioning has been addressed chiefly by iterative methods (e.g. Kernighan-Lin and Fiduccia-Mattheyses) and spectral methods (e.g. Hagen-Kahng). Iterative methods are the de facto industry standard, but suffer diminished stability and solution quality when instances grow large. Spectral methods have achieved high-quality solutions, particularly for the ratio cut objective, but suffer excessive memory requirements and the inability to capture practical constraints (preplacements, variable module areas, etc.). This work develops a new approach to Fiduccia-Mattheyses (FM)-based iterative partitioning. We combine two concepts: (1) problem reduction using clustering and the two-phase FM methodology and (2) adaptive multistart, i.e. the intelligent selection of starting points for the iterative optimization, based on the results of previous optimizations. The resulting clustered adaptive multistart (CAMS) methodology substantially improves upon previous partitioning results in the literature, for both unit module areas and actual module areas, and for both the min-cut bisection and minimum ratio cut objectives. The CAMS method is surprisingly fast and has very stable solution quality, even for large benchmark instances. It has been applied as the basis of a clustering methodology within an industry placement tool