A framework for dynamic parallelization of FPGA-accelerated applications

High-level synthesis and compiler studies have introduced many compile-time techniques for parallelizing applications. However, one fundamental limitation of compile-time optimization is the requirement for pessimistic dependence assumptions that can significantly restrict parallelism. To avoid this limitation, many compilers require a restrictive coding style that is not practical for many designers. We present a more transparent approach that aggressively parallelizes applications by dynamically analyzing actual runtime dependencies and scheduling functions onto multiple devices when dependencies allow. In addition, the approach applies FPGA-specific pipelining optimizations to exploit deep parallelism in chains of dependent functions. Experimental results show a speedup of 4.9x for a video-processing application compared to sequential software execution, a speedup of 5.6x compared to traditional FPGA execution, with a framework overhead of only 4%.