A clustering- and probability-based approach for time-multiplexed FPGA partitioning

Improving logic density by time-sharing, time-multiplexed FPGAs (TMFPGAs) has become an important research topic for reconfigurable computing. Due to the precedence and capacity constraints in TMFPGAs, the clustering and partitioning problems for TMFPGAs are different from the traditional ones. We propose a two-phase hierarchical approach to solve the partitioning problem for TMFPGAs. With the precedence and capacity considerations for both phases, the first phase clusters nodes to reduce the problem size, and the second phase applies a probability-based iterative improvement approach to minimize cut cost. Experimental results based on the Xilinx TMFPGA architecture show that our algorithm significantly outperforms previous works.