Using C-to-gates to program streaming image processing kernels efficiently on FPGAs

Effectively exploiting the variety of computational and storage resources available in common FPGA architectures for complex applications, such as the real-time implementation of vision algorithms, is often difficult in standard HDL design methodologies. Higher-level design tools can enable a design to more quickly explore a range of different architectures. In this paper we apply algorithmic C-to-FPGA synthesis technology in a structured design approach and demonstrate its added value on two relevant vision processing kernels: optical flow and debayering. The impact of the proposed approach on the design time, the FPGA resource consumption and the throughput is measured.