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 few last decades. More recently, several parametric dataflow MoCs have been presented as an
interesting trade-off between analyzability and expressiveness. They offer a controlled form of dynamism under the form of parameters (e.g.,
parametric rates), along with run-time 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 to choose the most suitable model for their needs and to pave the way for the design of new
parametric MoCs.