Exploiting Stable Data Dependency in Stream Processing Acceleration on FPGAs

Yuliang Sun, Lanjun Wang, Chen Wang, Yu Wang

Abstract: With the unique feature of fine-grained parallelism, field-programmable gate arrays (FPGAs) show great potential for streaming algorithm acceleration. However, the lack of a design framework, restrictions on FPGAs, and ineffective tools impede the utilization of FPGAs in practice. In this study, we provide a design paradigm to support streaming algorithm acceleration on FPGAs. We first propose an abstract model to describe streaming algorithms with homogeneous sub-functions (HSF) and stable data dependency (SDD), which we call the HSF-SDD model. Using this model, we then develop an FPGA framework, PE-Ring, that has the advantages of (1) fully exploiting algorithm parallelism to achieve high performance, (2) leveraging block RAM to serve large scale parameters, and (3) enabling flexible parameter adjustments. Based on the proposed model and framework, we finally implement a specific converter to generate the register-transfer level representation of the PE-Ring. Experimental results show that our method outperforms ordinary FPGA design tools by one to two orders of magnitude. Experiments also demonstrate the scalability of the PE-Ring.