A Survey of Parametric Dataflow Models of Computation

Abstract

Dataflow models of computation (MoCs) are widely used to design embedded signal processing and streaming systems. Dozens of dataflow MoCs have been proposed in the past few decades. More recently, several parametric dataflow MoCs have been presented as an interesting tradeoff between analyzability and expressiveness. They offer a controlled form of dynamism under the form of parameters (e.g., parametric rates), along with runtime parameter configuration. This survey provides a comprehensive description of the existing parametric dataflow MoCs (constructs, constraints, properties, static analyses) and compares them using a common example. The main objectives are to help designers of streaming applications choose the most suitable model for their needs and pave the way for the design of new parametric MoCs.