Investigación

Publicación (Conferencias y Seminarios)

Delays and states in dataflow models of computation

Arrestier, Florian; Desnos, Karol; Pelcat, Maxime; Heulot, Julien; Juárez Martínez, Eduardo; Menard, Daniel
Resumen:
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. In this paper, a meta-model called State-Aware Dataflow (SAD) is proposed that enhances a dataflow MoC, introducing new semantics to take advantage of such nested loop parallelism. 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 use-case.
Áreas de investigación:
Año:
2018
Tipo de publicación:
Conferencias y Seminarios
Palabras clave:
Computer systems organization; CPS
Organización:
SAMOS '18: Proceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation
Mes:
Julio
DOI:
10.1145/3229631.3229645
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