Abstract—Dataflow has proven to be an attractive computation
model for programming digital signal processing (DSP) appli-
cations. A restricted version of dataflow, termed synchronous
dataflow (SDF), that offers strong compile-time predictability
properties, but has limited expressive power, has been studied
extensively in the DSP context. Many extensions to synchronous
dataflow have been proposed to increase its expressivity while
maintaining its compile-time predictability properties as much
as possible. We propose a parameterized dataflow framework
that can be applied as a meta-modeling technique to significantly
improve the expressive power of any dataflow model that pos-
sesses a well-defined concept of a graph iteration. Indeed, the
parameterized dataflow framework is compatible with many of
the existing dataflow models for DSP including SDF, cyclo-static
dataflow, scalable synchronous dataflow, and Boolean dataflow.In
this paper, we develop precise, formal semantics for parameterized
synchronous dataflow (PSDF)—the application of our parameter-
ized modeling framework to SDF—that allows data-dependent,
dynamic DSP systems to be modeled in a natural and intuitive
fashion. Through our development of PSDF, we demonstrate
that desirable properties of a DSP modeling environment such as
dynamic reconfigurability and design reuse emerge as inherent
characteristics of our parameterized framework. An example of
a speech compression application is used to illustrate the efficacy
of the PSDF approach and its amenability to efficient software
synthesis techniques. In addition, we illustrate the generality of
our parameterized framework by discussing its application to
cyclostatic dataflow, which is a popular alternative to the SDF
model.