Enhancements to FPGA design methodology using streaming

Capacity of FPGAs has grown significantly, leading to increased complexity of designs targeting these chips. Traditional FPGA design methodology using HDLs is no longer sufficient and new methodologies are being sought. An attractive possibility is to use streaming languages. Streaming languages group data into streams, which are processed by computational nodes called kernels. They are suitable for implementation in FPGAs because they expose parallelism, which can be exploited by implementing the application in FPGA logic. Designers can express their designs in a streaming language and target FPGAs without needing a detailed understanding of digital logic design. In this paper we show how the Brook streaming language can be used to simplify design for FPGAs, while providing reasonable performance compared to other methodologies. We show that throughput of streaming applications can be increased through automatic kernel replication. Using our compiler, the FPGA designer can trade off FPGA area and performance by changing the amount of kernel replication. We describe the details of our compiler and present performance and area of a set of benchmarks. We found that throughput scales well with increased replication for most applications.