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A Survey of Parametric Dataflow Models of Computation

Published: 20 January 2017 Publication History

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 past few decades. More recently, several parametric dataflow MoCs have been presented as an interesting tradeoff between analyzability and expressiveness. They offer a controlled form of dynamism under the form of parameters (e.g., parametric rates), along with runtime 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 choose the most suitable model for their needs and pave the way for the design of new parametric MoCs.

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  • (2023)A Model of Design for Computing Systems: A Categorical ApproachIEEE Access10.1109/ACCESS.2023.332534911(116304-116347)Online publication date: 2023
  • (2022)RDF: A Reconfigurable Dataflow Model of ComputationACM Transactions on Embedded Computing Systems10.1145/354497222:1(1-30)Online publication date: 29-Oct-2022
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    cover image ACM Transactions on Design Automation of Electronic Systems
    ACM Transactions on Design Automation of Electronic Systems  Volume 22, Issue 2
    Special Section of IDEA: Integrating Dataflow, Embedded Computing, and Architecture
    April 2017
    458 pages
    ISSN:1084-4309
    EISSN:1557-7309
    DOI:10.1145/3029795
    • Editor:
    • Naehyuck Chang
    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].

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    Association for Computing Machinery

    New York, NY, United States

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    Publication History

    Published: 20 January 2017
    Accepted: 01 September 2016
    Revised: 01 July 2016
    Received: 01 January 2016
    Published in TODAES Volume 22, Issue 2

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    Author Tags

    1. Dataflow graphs
    2. parameterization
    3. reconfiguration
    4. static analysis

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    Cited By

    View all
    • (2023)A Model of Design for Computing Systems: A Categorical ApproachIEEE Access10.1109/ACCESS.2023.332534911(116304-116347)Online publication date: 2023
    • (2022)RDF: A Reconfigurable Dataflow Model of ComputationACM Transactions on Embedded Computing Systems10.1145/354497222:1(1-30)Online publication date: 29-Oct-2022
    • (2022)Influence of Dataflow Graph Moldable Parameters on Optimization CriteriaDesign and Architecture for Signal and Image Processing10.1007/978-3-031-12748-9_7(83-95)Online publication date: 20-Jun-2022
    • (2021)Analytical Performance Estimation for Large-Scale Reconfigurable Dataflow PlatformsACM Transactions on Reconfigurable Technology and Systems10.1145/345274214:3(1-21)Online publication date: 12-Aug-2021
    • (2021)Recovery of Parallel Dataflow Computing System From Faults and Failures2021 IEEE East-West Design & Test Symposium (EWDTS)10.1109/EWDTS52692.2021.9581026(1-6)Online publication date: 10-Sep-2021
    • (2020)Design and Implementation of Reconfigurable Asynchronous PipelinesIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2020.2975591(1-13)Online publication date: 2020
    • (2019)RDF: Reconfigurable Dataflow2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2019.8714987(1709-1714)Online publication date: Mar-2019

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