Abstract

Abstract-Dataflow-based application specifications are widely used in model-based design methodologies for signal processing systems. In this paper, we develop a new model called the dataflow schedule graph (DSG) for representing a broad class of dataflow graph schedules. The DSG provides a graphical representation of schedules based on dataflow semantics. In conventional approaches, applications are represented using dataflow graphs, whereas schedules for the graphs are represented using specialized notations, such as various kinds of sequences or looping constructs. In contrast, the DSG approach employs dataflow graphs for representing both application models and schedules that are derived from them.Our DSG approach provides a precise, formal framework for unambiguously representing, analyzing, manipulating, and interchanging schedules. We develop detailed formulations of the DSG representation, and present examples and experimental results that demonstrate the utility of DSGs in the context of heterogeneous signal processing system design.

Abstract. Dataflow formalisms have provided designers of digital signal processing systems with analysis and optimizations for many years. As system complexity increases, designers are relying on more types of dataflow models to describe applications while retaining these implementation benefits. The semantic range of DSP-oriented dataflow models has expanded to cover heterogeneous models and dynamic applications, but efficient design, simulation, and scheduling of such applications has not. To facilitate implementing heterogeneous applications, we utilize a new dataflow model of computation and show how actors designed in other dataflow models are directly supported by this framework, allowing system designers to immediately compose and simulate actors from different models. Using an example, we show how this approach can be applied to quickly describe and functionally simulate a heterogeneous dataflowbased application such that a designer may analyze and tune trade-offs among different models and schedules for simulation time, memory consumption, and schedule size.

Abstract-DICE (the DSPCAD Integrative CommandLine Environment) is a package of utilities that facilitates efficient management of software projects. Key areas of emphasis in DICE are cross-platform operation, support for projects that integrate heterogeneous programming languages, and support for applying and integrating different kinds of design and testing methodologies. The package is being developed at the University of Maryland to facilitate the research and teaching of methods for implementation, testing, evolution, and revision of engineering software. The package is also being developed as a foundation for developing experimental research software for techniques and tools in the area of computeraided design (CAD) of digital signal processing (DSP) systems. The package is intended for cross-platform operation, and is currently being developed and used actively on the Linux, Mac OS, Solaris, and Windows (equipped with Cygwin) platforms.This report provides an introduction to DICE, and provides background on some of the key features in DICE Version 1.1. This report also gives a brief introduction to dicelang, which is a plug-in package for DICE that provides additional utilities, libraries, and tools for managing software projects in specific programming languages.

For a number of years, dataflow concepts have provided designers of digital signal processing systems with environments capable of expressing high-level software architectures as well as low-level, performance-oriented kernels. To apply these proven techniques to new complex, dynamic applications, we identify repetitive sequences of atomic, repeatable actions ("modes") inside dynamic actors to expose more of the static nature of the application. In this work, we propose a mode grouping strategy that aids in the decomposition of a dynamic dataflow graph into a set of static dataflow graphs that interact dynamically. Mode grouping enables the discovery of larger static subgraphs improving scheduling results. We show that grouping modes results in improved schedules with lower memory requirements for implementations by up to 37% including a common imaging benchmark with dynamic behavior: 3D B-spline interpolation.