A general framework for vertex orderings, with applications to netlist clustering
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Partitioning very large circuits using analytical placement techniques
DAC '94 Proceedings of the 31st annual Design Automation Conference
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
Spectral partitioning: the more eigenvectors, the better
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
A probability-based approach to VLSI circuit partitioning
DAC '96 Proceedings of the 33rd annual Design Automation Conference
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
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In the spectral method, the vertices in a graph can be mapped into the vectors in d-dimensional space, thus the vectors are partitioned instead of vertices to obtain graph partitioning. In this paper, we show a method to obtain optimal two-way vector partitioning based on an optimal direction vector. As the problem to find the optimal direction vector is NP-problem, we propose an efficient heuristic to obtain high quality direction vector. As we approximate a given netlist into the graph and only use ten eigenvectors in practice, there is a chance to improve the solution quality by local optimization. Fiduccia-Mattheyses algorithm is employed as a post processing. Compared with FM and MELO, the proposed algorithm PDV reduces cutsize on the average 40% and 20.5%, respectively.