No abstract

FPGA devices have often found use as higher-performance alternatives to programmable processors for implementing computations. Applications successfully implemented on FPGAs typically contain high levels of parallelism and often use simple statically scheduled control and modest arithmetic. Recently introduced computing devices such as coarse-grain reconfigurable arrays, multi-core processors, and graphical processing units promise to significantly change the computational landscape and 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 because of its high computational and data rate requirements, and so is a good candidate for implementation on FPGAs and other custom computing architectures. This article reports on a series of experiments mapping a collection of different algorithms onto both an FPGA and a GPU. For two different optical flow algorithms the GPU had better performance, while for a set of digital comm MIMO computations, they had similar performance. In all cases the FPGA implementations required 10x the development time. Finally, a discussion of the two technology’s characteristics is given to show they achieve high performance in different ways.