In recent work, a graphical modeling construct called “topological patterns” has been shown to enable concise representation and direct analysis of repetitive dataflow graph sub-structures in the context of design methods and tools for digital signal processing systems. In this paper, we present a formal design method for specifying topological patterns and deriving parameterized schedules from such patterns based on a novel schedule model called the scalable schedule tree. The approach represents an important class of parameterized schedule structures in a form that is intuitive for representation and efficient for code generation. We demonstrate our methods for topological pattern representation, scalable schedule tree derivation, and associated dataflow graph code generation using a case study for image processing.

Development of multimedia systems that can be targeted to different platforms is challenging due to the need for rigorous integration between high-level abstract modeling, and low-level synthesis and optimization. In this paper, a new dataflow-based design tool called the targeted dataflow interchange format is introduced for retargetable design, analysis, and implementation of embedded software for multimedia systems. Our approach provides novel capabilities, based on principles of task-level dataflow analysis, for exploring and optimizing interactions across design components; object-oriented data structures for encapsulating contextual information for components; a novel model for representing parameterized schedules that are derived from repetitive graph structures; and automated code generation for programming interfaces and low-level customizations that are geared toward high-performance embedded-processing architectures. We demonstrate our design tool for cross-platform application design, parameterized schedule representation, and associated dataflow graph-code generation using a case study centered around an image registration application.

Sensor node processing in resource-aware sensor networks is often critically dependent on dynamic signal processing functionality - i.e., signal processing functionality in which computational structure must be dynamically assessed and adapted based on time-varying environmental conditions, operating constraints or application requirements. In dynamic signal processing systems, it is important to provide flexibility for run-time adaptation of application behavior and execution characteristics, but in the domain of resource-aware sensor networks, such flexibility cannot come with significant costs in terms of power consumption overhead or reduced predictability. In this paper, we review a variety of complementary models of computation that are being developed as part of the dataflow interchange format (DIF) project to facilitate efficient and reliable implementation of dynamic signal processing systems. We demonstrate these methods in the context of resource-aware sensor networks.

Development of multimedia systems on heterogeneous platforms is a challenging task with existing design tools due to a lack of rigorous integration between high level abstract modeling, and low level synthesis and analysis. In this paper, we present a new dataflow-based design tool, called the targeted dataflow interchange format (TDIF), for design, analysis, and implementation of embedded software for multimedia systems. Our approach provides novel capabilities, based on the principles of task-level dataflow analysis, for exploring and optimizing interactions across application behavior; operational context; heterogeneous platforms, including high performance embedded processing architectures; and implementation constraints.

The latest communication technologies invariably consist of modules with dynamic behavior. There exists a number of design tools for communication system design with their foundation in dataflow modeling semantics. These tools must not only support the functional specification of dynamic communication modules and subsystems but also provide accurate estimation of resource requirements for efficient simulation and implementation. We explore this trade-off - between flexible specification of dynamic behavior and accurate estimation of resource requirements - using a representative application employing an adaptive modulation scheme. We propose an approach for precise modeling of such applications based on a recently-introduced form of dynamic dataflow called core functional dataflow. From our proposed modeling approach, we show how parameterized looped schedules can be generated and analyzed to simulate applications with low run-time overhead as well as guaranteed bounded memory execution. We demonstrate our approach using the Advanced Design System from Agilent Technologies, Inc., which is a commercial tool for design and simulation of communication systems.

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.

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. But analysis of system-level trade-offs has been inhibited by the diversity of models and the dynamic nature of modern dataflow applications. To facilitate design space exploration for software implementations of heterogeneous dataflow applications, developers need tools capable of deeply analyzing and optimizing the application. To this end, we present a new scheduling approach that leverages a recently proposed general model of dynamic dataflow called core functional dataflow (CFDF). CFDF supports high-level application descriptions with multiple models of dataflow by structuring actors with sets of modes that represent fixed behaviors. In this work we show that by decomposing a dynamic dataflow graph as directed by its modes, we can derive a set of static dataflow graphs that interact dynamically. This enables designers to readily experiment with existing dataflow model specific scheduling techniques to all or some parts of the application while applying custom schedulers to others. We demonstrate this generalized dataflow scheduling method on dynamic mixed-model applications and show that run-time and buffer sizes significantly improve compared to a baseline dynamic dataflow scheduler and simulator.