An enhanced evolutionary approach to spatial partitioning for reconfigurable environments

This paper introduces a novel parallel evolutionary methodology making use of ANN for solving the spatial partitioning problem for multi-FPGA (field programmable gate arrays) architectures. The algorithm takes as input a HDL (hardware description language) model of the application along with user specified constraints and automatically generates a task graph G; partitions G based on the user specified constraints and maps the blocks of the partitions onto the different FPGAs in the given multi-FPGA architecture, all in a single-shot. The proposed algorithm was successfully employed to spatially partition a reasonably big cryptographic application that involved a 1024-bit modular exponentiation and to map the same onto a network of nine ACEX1K based Altera EP1K30QC208-1 FPGAs. The suggested parallel evolutionary algorithm for the partitioning step was implemented on a 6-node SGI Origin-2000 platform using the message passing interface (MPI) standard. The results obtained by executing the same are extremely encouraging, especially for larger task graphs.