Reactive process networks

Geilen, M.C.W.; Basten, Twan

doi:10.1145/1017753.1017778

Cited by 47 publications

(28 citation statements)

References 14 publications

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“…For RPN, a denotational semantics in terms of input/output relations is not possible (Section 7) and one based on sequential execution traces is possible, but hides the parallelism of the model. The detailed operational semantics of RPN can be found in [20]. In the next section, we discuss the main concepts.…”

Section: Semantic Modelmentioning

confidence: 99%

“…An RPN, like a KPN, consists of processes, which may in turn be other RPNs, or primitive processes defined through other means, such as sequential code segments, and the LTS is constructed compositionally, similar to the operational semantics of KPN in Section 3. A detailed description of the operational semantics of RPN can be found in [20]; here we concentrate on the important concepts.…”

Section: Operational Semantics Of Rpnmentioning

confidence: 99%

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Kahn Process Networks and a Reactive Extension

Geilen

Basten

2010

Handbook of Signal Processing Systems

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Kahn and MacQueen have introduced a generic class of determinate asynchronous data-flow applications, called Kahn Process Networks (KPNs) with an elegant mathematical model and semantics in terms of Scott-continuous functions on data streams together with an implementation model of independent asynchronous sequential programs communicating through FIFO buffers with blocking read and non-blocking write operations. The two are related by the Kahn Principle which states that a realization according to the implementation model behaves as predicted by the mathematical function. Additional steps are required to arrive at an actual implementation of a KPN to take care of scheduling of independent processes on a single processor and to manage communication buffers. Because of the expressiveness of the KPN model, buffer sizes and schedules cannot be determined at design time in general and require dynamic run-time system support. Constraints are discussed that need to be placed on such system support so as to maintain the Kahn Principle. We then discuss a possible extension of the KPN model to include the possibility for sporadic, reactive behavior which is not possible in the standard model. The extended model is called Reactive Process Networks. We introduce its semantics, look at analyzability and at more constrained data-flow models combined with reactive behavior.

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Section: Semantic Modelmentioning

confidence: 99%

Section: Operational Semantics Of Rpnmentioning

confidence: 99%

Kahn Process Networks and a Reactive Extension

Geilen

Basten

2010

Handbook of Signal Processing Systems

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“…An approach to handle dynamic behavior (dynamic stream processing) has been proposed by Geilen and Basten in [32]. In [24], the authors present a new approach to express and analyze implementation specific aspects in CSDF graphs -a primary focus of this work -for computer vision applications with concentration only on the channel/edge implementation.…”

Section: Modeling and Transformationsmentioning

confidence: 99%

Design Methodology for Embedded Computer Vision Systems

Bhattacharyya

Saha

2009

Embedded Computer Vision

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Computer vision has emerged as one of the most popular domains of embedded applications. Though various new powerful embedded platforms to support such applications have emerged in recent years, there is a distinct lack of efficient domain-specific synthesis techniques for optimized implementation of such systems. In this thesis, four different aspects that contribute to efficient design and synthesis of such systems are explored:(1) Graph Transformations: Dataflow modeling is widely used in digital signal processing (DSP) systems. However, support for dynamic behavior in such systems exists mainly at the modeling level and there is a lack of optimized synthesis techniques for these models. New transformation techniques for efficient system-on-chip (SoC) design methods are proposed and implemented for cyclo-static dataflow and its parameterized version (parameterized cyclo-static dataflow) -two powerful models that allow dynamic reconfigurability and phased behavior in DSP systems.

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“…Several researchers have proposed extensions to the KPN model of computation to resolve this [7][8][9]. Our two multiapplication workload modeling techniques support the modeling of reactive behavior between applications, which could each be specified as a regular KPN.…”

Section: Modeling Reactive Behaviormentioning

confidence: 99%

Towards Multi-application Workload Modeling in Sesame for System-Level Design Space Exploration

Thompson

Pimentel

Lecture Notes in Computer Science

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Abstract. The Sesame modeling and simulation framework aims at early and thus efficient system-level design space exploration of embedded multimedia system architectures. So far, Sesame only supported performance evaluation when mapping a single application onto a (multi-processor) architecture at the time. But since modern multimedia embedded systems are increasingly multi-tasking, we need to address the modeling of effects of executing multiple applications concurrently in our system-level performance models. To this end, this paper conceptually describes two multi-application workload modeling techniques for the Sesame framework. One technique is based on the use of synthetic application workloads while the second technique deploys only real application workloads to model concurrent execution of applications. For illustrative purposes, we also present a preliminary case study in which a Motion-JPEG encoder application is executed concurrently with a small synthetic producer-consumer application.

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Reactive process networks

Cited by 47 publications

References 14 publications

Kahn Process Networks and a Reactive Extension

Kahn Process Networks and a Reactive Extension

Design Methodology for Embedded Computer Vision Systems

Towards Multi-application Workload Modeling in Sesame for System-Level Design Space Exploration

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