Scenario-Aware Dataflow

This section1 discusses Scenario-Aware Dataflow (SADF), which is a generalization of dataflow models with strict periodic behavior. Like many dataflow models, SADF is primarily a coordination language that highlights how actors (which are potentially executed in parallel) interact. To express dynamism, SADF distinguishes data and control explicitly, where the control-related coherency between the behavior (and hence, the resource requirements) of different parts of a signal processing application can be captured with so-called scenarios [6]. The scenarios commonly coincide with dissimilar (but within themselves more static) modes of operation originating, for example, from different parameter settings, sample rate conversion factors, or the signal processing operations to perform. Scenarios are typically defined by clustering operational situations with similar resource requirements [6]. The scenario-concept in SADF allows for more precise (quantitative) analysis results compared to applying traditional SDF-based analysis techniques. Still, common subclasses of SADF can be synthesized into efficient implementations [20, 9].