Parameterized scheduling for signal processing systems using topological patterns

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 [1]. 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.