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Markov clustering-based placement algorithm for hierarchical FPGAs

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3 Author(s)
Dai, Hui ; Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China ; Zhou, Qiang ; Bian, Jinian

Divide-and-conquer methods for FPGA placement algorithms including partition-based and cluster-based algorithms have shown the importance of good quality-runtime trade-off. This paper describes a cluster-based FPGA placement algorithm targeted to a new commercial hierarchical FPGA device. The algorithm is based on a Markov clustering algorithm that defines a sequence of stochastic matrices operating on a generating matrix from the input FPGA circuit netlist. The core of the algorithm tightly couples a Markov clustering process with a multilevel placement process. Tests show its excellent adaptability to hierarchical FPGAs. The average wirelength results produced by the algorithm are 22.3% shorter than the results produced by the current hierarchical FPGA placer.

Published in:

Tsinghua Science and Technology  (Volume:16 ,  Issue: 1 )