Communication storage optimization for static dataflow with access patterns under periodic scheduling and throughput constraint
We address a recently introduced static dataflow model: the Static Dataflow with Access Patterns (SDF-AP) model. For this model we present (1) a generalization of an existing regular periodic scheduling scheme to regular 1-periodic scheduling for flexibility to achieve a smaller schedule period and additional room for optimization on communication storage; (2) a method based on Integer Linear Programming (ILP) to minimize communication buffers under periodic scheduling and user-specified throughput constraints. Experimental results on a set of test cases show that buffer sizes using this approach can be reduced dramatically when compared to the traditional SDF models. The optimal sizing result may serve as an important criterion to evaluate and fine-tune any heuristics-based buffer sizing approach for the SDF-AP model of computation.