A Survey of Parametric Dataflow Models of Computation

Abstract : Dataflow models of computation (MoCs) are widely used to design embedded signal processing and streaming systems. Dozens of dataflow MoCs have been proposed in the few last decades. More recently, several parametric dataflow MoCs have been presented as an interesting trade-off between analyzability and expressiveness. They offer a controlled form of dynamism under the form of parameters (e.g., parametric rates), along with run-time parameter configuration. This survey provides a comprehensive description of the existing parametric dataflow MoCs (constructs, constraints, properties, static analyses) and compares them using a common example. The main objectives are to help designers of streaming applications to choose the most suitable model for their needs and to pave the way for the design of new parametric MoCs.
Type de document :
Article dans une revue
ACM Transactions on Design Automation of Electronic Systems (TODAES), ACM, 2017
Liste complète des métadonnées

https://hal.inria.fr/hal-01417126
Contributeur : Pascal Fradet <>
Soumis le : jeudi 15 décembre 2016 - 12:17:14
Dernière modification le : jeudi 15 juin 2017 - 09:08:47

Identifiants

  • HAL Id : hal-01417126, version 1

Collections

Citation

Adnan Bouakaz, Pascal Fradet, Alain Girault. A Survey of Parametric Dataflow Models of Computation. ACM Transactions on Design Automation of Electronic Systems (TODAES), ACM, 2017. 〈hal-01417126〉

Partager

Métriques

Consultations de la notice

296