ShuntFlowPlus: An Efficient and Scalable Dataflow Accelerator Architecture for Stream Applications

Streaming processing is an important and growing class of applications for analyzing continuous streams in real time. In such applications, sliding-window aggregation (SWAG) is a widely used approach, and generalpurpose processors cannot efficiently handle SWAG because of the specific computation patterns. This article proposes an efficient dataflow accelerator architecture for ubiquitous SWAGs, called ShuntFlowPlus. ShuntFlowPlus supports two main categories of SWAGs that are widely used in streaming processing. Meanwhile, we propose a shunt rule to enable ShuntFlowPlus to efficiently handle SWAGs with arbitrary parameters. Furthermore, we propose a novel realization scheme of SWAG kernels based on buffer sharing to maximize buffer utilization. As a case study, we implemented ShuntFlowPlus on an Altera Arria 10 AX115N FPGA board at 150 MHz and compared it to previous approaches. The experimental results show that ShuntFlowPlus provides a tremendous throughput and latency advantage over CPU and GPU implementations on both reducelike and index-like SWAGs. Compare to ShuntFlow, 41% of buffer resources are saved.