Parameterized dataflow modeling of DSP systems

Dataflow has proven to be an attractive computation model for programming DSP applications. A restricted version of dataflow, termed synchronous dataflow (SDF), that offers strong compiletime predictability properties, but has limited expressive power, has been studied extensively in the DSP context [3]. 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 an arbitrary dataflow model that possesses 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, CSDF, and SSDF. In this paper, we develop a precise, formal semantics for parameterized synchronous dataflow that allows data-dependent dynamic DSP systems to be modeled in a natural and intuitive fashion. Desirable properties of a modeling environment like dynamic re-configurability and design re-use emerge as inherent characteristics of the parameterized framework. An example of a speech compression application is used to illustrate the efficacy of the parameterized modeling techniques in real-life data-dependent DSP systems.