Dataflow Models of Computation (MoCs) have proven efficient means for modeling computational aspects of Cyber-Physical System (CPS). Over the years, diverse MoCs have been proposed that offer trade-offs between expressivity, conciseness, predictability, and reconfigurability. While being efficient for modeling coarse grain data and task parallelism, state-of-theart dataflow MoCs suffer from a lack of semantics to benefit from the lower grained parallelism offered by hierarchically modeled nested loops. State-Aware Dataflow (SAD) extends the semantics of the targeted MoC with unambiguous data persistence scope. The extended expressiveness and conciseness brought by the SAD meta-model are demonstrated with a reinforcement learning usecase.
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