high performance computing on graphics processing units: hgpu.org

FPGA devices have often found use as higher-performance alternatives to programmable processors for implementing a variety of computations. Applications successfully implemented on FPGAs have typically contained high levels of parallelism and have often used simple statically-scheduled control and modest arithmetic. Recently introduced computing devices such as coarse grain reconfigurable arrays, multi-core processors, and graphical processing units (GPUs) promise to significantly change the computational landscape for the implementation of high-speed real-time computing tasks. One reason for this is that these architectures take advantage of many of the same application characteristics that fit well on FPGAs. One real-time computing task, optical flow, is difficult to apply in robotic vision applications in practice because of its high computational and data rate requirements, and so is a good candidate for implementation on FPGAs and other custom computing architectures. In this paper, a tensor-based optical flow algorithm is implemented on both an FPGA and a GPU and the two implementations discussed. The two implementations had similar performance, but with the FPGA implementation requiring 12x more development time. Other comparison data for these two technologies is then given for three additional applications taken from a MIMO digital communication system design, providing additional examples of the relative capabilities of these two technologies.