Throughput optimization for streaming applications on CPU-FPGA heterogeneous systems

Streaming processing is an important technology that finds applications in networking, multimedia, signal processing, etc. However, it is very challenging to design and implement streaming applications as they impose complex constraints. First, the tasks involved in the streaming applications must complete the computation under a latency constraint. Second, streaming systems are built under more and more stringent power budget. Hence, power capping technique is employed to manage the power consumption for streaming systems. To accommodate these needs, heterogeneous systems that consist of CPUs and FPGAs are becoming increasingly popular due to their performance and power benefits. In this paper, we optimize the throughput for streaming applications on CPU-FPGA heterogeneous system under latency and power constraints. We develop two algorithms to map the tasks onto the heterogeneous system and order their execution by exploiting the heterogeneity in architectural capabilities and task characteristics. We also employ pipelining to improve the throughput by overlapping the execution of different frames and use frequency scaling to adjust the execution of tasks for power saving. Experiments using a variety of streaming applications show that our heterogeneous solution can successfully meet the latency and power constraints for the cases where the CPU implementation fails. Furthermore, our technique can improve the throughput by 37.32% on average.