RDF: A Reconfigurable Dataflow Model of Computation - Laboratoire d'Informatique de Grenoble Accéder directement au contenu
Article Dans Une Revue ACM Transactions on Embedded Computing Systems (TECS) Année : 2022

RDF: A Reconfigurable Dataflow Model of Computation

Résumé

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 first and 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 lack 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 and when 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 present its implementation and some experimental results.
Fichier principal
Vignette du fichier
final_RDF.pdf (830.2 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03940615 , version 1 (16-01-2023)

Identifiants

Citer

Pascal Fradet, Alain Girault, Ruby Krishnaswamy, Xavier Nicollin, Arash Shafiei. RDF: A Reconfigurable Dataflow Model of Computation. ACM Transactions on Embedded Computing Systems (TECS), 2022, ⟨10.1145/3544972⟩. ⟨hal-03940615⟩
44 Consultations
43 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More