Abstract-Dataflow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. 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 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, 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 parameterized 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. Index Terms-CAD tools, dataflow modeling, embedded systems, reconfigurable design, software synthesis. T HE INCREASING use of configurable hardware techniques in digital signal processing (DSP) system design, along with the continual trend toward more dynamic behavior and reconfigurability in DSP applications, is leading to a view of DSP system design as the joint design of architectures (design substrates), parameterizations (design options that are exposed to higher levels of abstraction), and configurations (sets of design choices for the relevant parameterizations) [6] , as illustrated in Publisher Item Identifier S 1053-587X(01)07764-9. on a programmable DSP processor can be described as the codesign of the microarchitecture (the architecture), the instruction set (a parameterization of the architecture), and the software that configures the microarchitecture/instruction set pair for the set of application functions. As another example, the design of a digital filtering subroutine can be viewed as the codesign of a software template (e.g., a sequence of for loops), a set of template parameters (e.g., the number of taps, and the filter coefficients), and a set of anticipated parameter value combinations that will frequently be used. This trend toward viewing design in terms of configurations of parameterized substrates is rapidly transforming the once-clear separation between hardware and software into a continuum of tradeoffs between specialization and flexibility. It is thus becoming increasingly important to provide precise and powerful mechanisms for modeling parameterization and configurability in design tools for DSP systems. Motivated by this growing need, we develop, in this paper, formal semantics for modeling DSP applications that captures the codesign relationships of Fig. 1 and leads to efficient techniques for automated synthesis of implementations. In particular, we introduce parameterized dataflow as a meta-modeling technique that can be applied to a wide range of dataflow models to significantly increase their expressive power, such that data-dependent, dynamically reconfigurable DSP systems can be expressed in a natural and intuitive fashion. Our parameterization concepts can be incorporated into any dataflow model in which there is a notion of a graph iteration. For example, the parameterized framework is compatible with many of the existing dataflow models such as synchronous dataflow [19] , scalable synchronous dataflow [25] , cyclo-static dataflow [9] , and Boolean dataflow [11] . For clarity and uniformity, and because SDF is currently the most popular and widely studied dataflow model for DSP, we develop parameterized dataflow formally in the context of SDF [called parameterized synchronous dataflow (PSDF)]. In addition to the formal model, we have also developed efficient software synthesis techniques (referred to as quasi-static scheduling) for a class of PSDF specifications, which is discussed in Section VI-A. A speech compression application modeled in Section VIII shows an example of applying PSDF techniques to real-life DSP designs. In Section IX, we illustrate the generality of the parameterized dataflow framework by dis-
In REF , a meta-modeling technique is proposed to augment the expressive power of wide range of existing data-flow models which have the graph iteration concept.
Parameterized Dataflow Modeling for DSP Systems
IEEE Transactions on Signal Processing
Computer Science