Cycle-static dataflow

Bilsen, G.; Engels, M.; Lauwereins, Rudy; Peperstraete, Jean

doi:10.1109/78.485935

Cited by 417 publications

(396 citation statements)

References 14 publications

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“…Cyclo-Static Dataflow (CSDF) is a MoC for describing applications in the digital signal processing domain [4]. It extends the popular Synchronous Data Flow (SDF) model proposed in [14].…”

Section: The Cyclo-static Dataflow (Csdf) Modelmentioning

confidence: 99%

“…Bilsen et al defined the CSDF model in [4] as a directed graph G = V, E , where V is a set of actors and E is a set of communication channels. Actors represent functions that transform the input data streams into output streams.…”

Section: Formal Definitionmentioning

confidence: 99%

“…Assume that the CSDF graph has an execution vector µ = [5,2,3,2] T . Given q = [3,3,6,4] T as computed in Example 2, we use Equation 11 to find λ min = [8,8,4,6] T . Figure 6 illustrates the periodic schedule of the actors for the first 71 time-units.…”

Section: Algorithm 1 Acyclic-csdf-graph-levels(g)mentioning

confidence: 99%

“…Given a connected CSDF graph G, a valid static schedule for G is a schedule that can be repeated infinitely on the incoming sample stream and where the amount of data in the buffers remains bounded. A vector q = [q1, q2, · · · , qN ] T , where qj > 0, is a repetition vector of G if each qj represents the number of invocations of an actor vj in a valid static schedule for G. [4])…”

Section: Compile-time Schedulingmentioning

confidence: 99%

“…throughput). One particular MoC that is popular in the embedded signal processing systems community is the Cyclo-Static Dataflow (CSDF) model [4] which extends the well-known Synchronous Data Flow (SDF) model [14].…”

Section: Introductionmentioning

confidence: 99%

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Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Bamakhrama

Stefanov

2011

Proceedings of the Ninth ACM International Conference on Embedded Software

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Most of the hard-real-time scheduling theory for multiprocessor systems assumes independent periodic or sporadic tasks. Such a simple task model is not directly applicable to modern embedded streaming applications. This is because a modern streaming application is typically modeled as a directed graph where nodes represent actors (i.e. tasks) and edges represent data-dependencies. The actors in such graphs have data-dependency constraints and do not necessarily conform to the periodic or sporadic task models. Therefore, in this paper we investigate the applicability of hardreal-time scheduling theory for periodic tasks to streaming applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. In such graphs, the actors are data-dependent, however, we analytically prove that they (i.e. the actors) can be scheduled as implicitdeadline periodic tasks. As a result, a variety of hard-real-time scheduling algorithms for periodic tasks can be applied to schedule such applications with a certain guaranteed throughput. We compare the throughput resulting from such scheduling approach to the maximum achievable throughput of an application for a set of 19 real streaming applications. We find that in more than 80% of the cases, the throughput resulting from our approach is equal to the maximum achievable throughput.

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“…Cyclo-Static Dataflow (CSDF) is a MoC for describing applications in the digital signal processing domain [4]. It extends the popular Synchronous Data Flow (SDF) model proposed in [14].…”

Section: The Cyclo-static Dataflow (Csdf) Modelmentioning

confidence: 99%

Section: Formal Definitionmentioning

confidence: 99%

Section: Algorithm 1 Acyclic-csdf-graph-levels(g)mentioning

confidence: 99%

Section: Compile-time Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Bamakhrama

Stefanov

2011

Proceedings of the Ninth ACM International Conference on Embedded Software

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Final Statements

2008

FPGA‐Based Implementation of Signal Processing Systems

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Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging-in-Publication DataWood, Roger.FPGA-based implementation of complex signal processing systems / Roger Wood ... [et al.]. p. cm. Includes bibliographical references and index.

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Advanced Model‐Based FPGA Accelerator Design

2017

FPGA‐based Implementation of Signal Processing Systems

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Cycle-static dataflow

Cited by 417 publications

References 14 publications

Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Hard-real-time scheduling of data-dependent tasks in embedded streaming applications

Final Statements

Advanced Model‐Based FPGA Accelerator Design

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