RDF: Reconfigurable Dataflow

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the most popular da...

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Veröffentlicht in:Proceedings - Design, Automation, and Test in Europe Conference and Exhibition S. 1709 - 1714
Hauptverfasser: Fradet, Pascal, Girault, Alain, Krishnaswamy, Ruby, Nicollin, Xavier, Shafiei, Arash
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: EDAA 01.03.2019
Schlagworte:
Boundedness
Computational modeling
Liveness
Models of computation
Production
Reconfigurable systems
Resource description framework
Schedules
Static analyses
Static analysis
Synchronous dataflow
Synthetic aperture sonar
Topology
ISSN:1558-1101
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Zusammenfassung:Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF (and most of its variants) lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment.We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and outline its implementation.
ISSN:1558-1101
DOI:10.23919/DATE.2019.8714987