Compositional Dataflow Modelling for Cyclo-Static Applications

Modular design is a common practice when designing complex applications for embedded systems. Another important practice in the embedded systems domain is the use of abstract models to realize predictable behaviour. Modular model-based design allows to construct a modular model of a complex system via model composition. The model of computation considered in this paper is scenario-aware dataflow, a dataflow model that allows for dynamic behaviour. We model applications with behaviour that changes according to a periodic pattern. Composing models with periodic patterns results in a model with a periodic pattern with a common hyper-period. We propose an efficient algorithmic method to compose cyclo-static scenario-aware dataflow models by generating composite patterns in a concise representation. We show that our approach can automatically generate concise models of several real-life image processing applications.