Delays and states in dataflow models of computation
Florian Arrestier, Karol Desnos, Maxime Pelcat, Julien Heulot, Eduardo Juárez, Daniel Ménard · 2018
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-the-art dataflow MoCs suffer from a lack of semantics to benefit from the lower grained parallelism offered by hierarchically modeled nested loops.