Abstract
Hardware-software partitioning in an embedded multiprocessor field programmable gate array (FPGA) system is difficult as such systems are uncertain and constraints are various. Moreover, the effect of relational degree for each partitioning combination of system constraints is too difficult analysis to determine a partitioning result. This work applies grey relational analysis to identify a partitioning result for an uncertain system with multiple constraints. Moreover, the relational effect of each partitioning combination is applied to determine the role of each task as hardware or software. Experimental results indicate that the proposed attains a partitioning result with low power consumption and fast execution time for a benchmark with 199 tasks.
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