Efficient Pipelining of Synchronous Dataflow Graphs Via Graph Conversion

Abstract

Synchronous Dataflow graphs (SDFGs) are widely used to model streaming applications that exhibit data-driven and iterative execution patterns. Graph conversion techniques such as retiming, unfolding, and pipelining are commonly used to optimize the iteration periods (IPs) of SDFGs. In this paper, we propose an extension of the graph conversion based pipelining approach for single-rate SDFGs to multi-rate SDFGs. A new perspective on pipelining is introduced, where the pipelining of a general-time SDFG can be viewed as the retiming of a unit-time SDFG. Based on this perspective, we prove that optimal pipelining can always achieve an IP less than 1 time unit longer than the optimal IP for an SDFG. Furthermore, an efficient optimal SDFG pipelining algorithm called GCP-SDFG is presented. Experimental results show that GCP-SDFG has significant advantages in IP minimizing and runtime relative to three state-of-the-art retiming or pipelining algorithms.