skip to main content
research-article

RDF: A Reconfigurable Dataflow Model of Computation

Published:29 October 2022Publication History
Skip Abstract Section

Abstract

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.

REFERENCES

  1. [1] Battacharyya Shuvra S., Lee Edward A., and Murthy Praveen K.. 1996. Software Synthesis from Dataflow Graphs. Kluwer Academic Publishers, Norwell, MA.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Bebelis V., Fradet P., and Girault A.. 2014. A framework to schedule parametric dataflow applications on many-core platforms. In International Conference on Languages, Compilers and Tools for Embedded Systems (LCTES’14). ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Bebelis Vagelis, Fradet Pascal, Girault Alain, and Lavigueur Bruno. 2013. BPDF: A statically analyzable dataflow model with integer and Boolean parameters. In International Conference on Embedded Software (EMSOFT’13). 110.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Bhattacharya Bishnupriya and Bhattacharyya Shuvra S.. 2001. Parameterized dataflow modeling for DSP systems. IEEE Trans. Sig. Process. 49, 10 (2001), 24082421.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Bouakaz A., Fradet P., and Girault A.. 2016. Symbolic buffer sizing for throughput-optimal scheduling of dataflow graphs. In Real-Time and Embedded Technology and Applications Symposium (RTAS’16). 199208.Google ScholarGoogle Scholar
  6. [6] Bouakaz A., Fradet P., and Girault A.. 2017. A survey of parametric dataflow models of computation. ACM Trans. Des. Automat. Electron. Syst. 22, 2 (Mar. 2017).Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Bouakaz Adnan, Fradet Pascal, and Girault Alain. 2017. Symbolic analyses of dataflow graphs. ACM Trans. Des. Automat. Electron. Syst. 22, 2 (2017), 39.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Buck J. T. and Lee E. A.. 1993. Scheduling dynamic data-flow graphs with bounded memory using the token flow model. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP’93). IEEE, 429432.Google ScholarGoogle Scholar
  9. [9] Desnos K., Pelcat M., Nezan J.-F., Bhattacharyya S. S., and Aridhi S.. 2013. PiMM: Parameterized and interfaced dataflow meta-model for MPSoCs runtime reconfiguration. In International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS’13). IEEE, 4148.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Desnos Karol, Pelcat Maxime, Nezan Jean-François, Bhattacharyya Shuvra S., and Aridhi Slaheddine. 2013. PiMM: Parameterized and interfaced dataflow meta-model for MPSoCs runtime reconfiguration. In 13th International Conference on Embedded Computer Systems: Architecture, Modeling and Simulation. 4148.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Fradet Pascal, Girault Alain, Krishnaswamy Ruby, Nicollin Xavier, and Shafiei Arash. 2019. RDF: Reconfigurable dataflow. In Design, Automation & Test in Europe Conference & Exhibition.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Fradet Pascal, Girault Alain, and Poplavko Peter. 2011. SPDF: A Schedulable Parametric Data-flow MoC (Extended Version). Research Report RR-7828. INRIA. Retrieved from https://hal.inria.fr/hal-00666284.Google ScholarGoogle Scholar
  13. [13] Fradet Pascal, Girault Alain, and Poplavko Peter. 2012. SPDF: A schedulable parametric data-flow MoC. In Conference on Design, Automation and Test in Europe. 769774.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Garey M. R. and Johnson David S.. 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Geilen Marc. 2010. Synchronous dataflow scenarios. ACM Trans. Embed. Comput. Syst. 10, 2 (2010), 16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Jondral F.. 2005. Software-defined radio — Basics and evolution to cognitive radio. EURASIP J. Wirel. Commun. Netw. 3 (2005), 275283.Google ScholarGoogle Scholar
  17. [17] Kahn Gilles. 1974. The semantics of a simple language for parallel programming. Inf. Process. 74 (1974), 471475.Google ScholarGoogle Scholar
  18. [18] Lee E. A., Neuendorffer S., and Zhou G.. 2014. System Design, Modeling, and Simulation using Ptolemy II. Ptolemy.org.Google ScholarGoogle Scholar
  19. [19] Lee Edward A. and Messerschmitt David G.. 1987. Synchronous data flow. Proc. IEEE 75, 9 (1987), 12351245.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Li Jun, Dai Xianzhong, Meng Zhengda, and Xu Libo. 2008. Improved net rewriting systems-extended Petri nets supporting dynamic changes. J. Circ. Syst. Comput. 17, 6 (2008), 10271052.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Moreira O., Basten T., Geilen M., and Stuijk S.. 2010. Buffer sizing for rate-optimal single-rate data-flow scheduling revisited. IEEE Trans. Comput. 59, 2 (2010), 188201.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Padberg Julia and Kahloul Laïd. 2018. Overview of reconfigurable Petri nets. In Graph Transformation, Specifications, and Nets - In Memory of Hartmut Ehrig, Reiko Heckel and Gabriele Taentzer (Eds.). Lecture Notes in Computer Science, 201–222.Google ScholarGoogle Scholar
  23. [23] Padberg Julia and Schulz Alexander. 2016. Model checking reconfigurable Petri nets with Maude. In 9th International Conference on Graph Transformation (ICGT’16). 5470.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Raoult Jean-Claude and Voisin Frédéric. 1994. Set-theoretic graph rewriting. In Graph Transformations in Computer Science. Springer, 312325.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Shafiei Arash. 2021. RDF: A Reconfigurable Dataflow Model of Computation. Ph. D. Dissertation. Université Grenoble Alpes.Google ScholarGoogle Scholar
  26. [26] Skelin Mladen, Geilen Marc, Catthoor Francky, and Hendseth Sverre. 2015. Parametrized dataflow scenarios. In 12th International Conference on Embedded Software. 95104.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Wiggers Maarten H., Bekooij Marco J. G., and Smit Gerard J. M.. 2008. Buffer capacity computation for throughput constrained streaming applications with data-dependent inter-task communication. In IEEE Real-time and Embedded Technology and Applications Symposium. 183194.Google ScholarGoogle Scholar

Index Terms

  1. RDF: A Reconfigurable Dataflow Model of Computation

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Transactions on Embedded Computing Systems
        ACM Transactions on Embedded Computing Systems  Volume 22, Issue 1
        January 2023
        512 pages
        ISSN:1539-9087
        EISSN:1558-3465
        DOI:10.1145/3567467
        • Editor:
        • Tulika Mitra
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 October 2022
        • Online AM: 27 June 2022
        • Accepted: 11 June 2022
        • Revised: 15 April 2022
        • Received: 18 January 2022
        Published in tecs Volume 22, Issue 1

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Refereed
      • Article Metrics

        • Downloads (Last 12 months)118
        • Downloads (Last 6 weeks)10

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Full Text

      View this article in Full Text.

      View Full Text

      HTML Format

      View this article in HTML Format .

      View HTML Format
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!