Parametrized dataflow scenarios

Skelin, Mladen; Geilen, Marc; Catthoor, Francky; Hendseth, Sverre

doi:10.1109/emsoft.2015.7318264

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…More recently, several parametric dataow MoCs have been presented as an interesting trade-o between expressiveness and analyzability. Among the existing parametric MoCs let us cite PSDF [5], VRDF [28], SPDF [14], BPDF [3], PiSDF [11], and PFSM-SADF [27]. They all oer a controlled form of dynamism under the form of parameters (e.g., parametric rates) along with run-time parameter conguration.…”

Section: Related Workmentioning

confidence: 99%

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RDF: A Reconfigurable Dataflow Model of Computation

Fradet

Girault

Krishnaswamy

et al. 2022

ACM Trans. Embed. Comput. Syst.

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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 lacks 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.

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Section: Related Workmentioning

confidence: 99%

“…A dierent approach is taken by SADF [16] and its parametric version PFSM-SADF [27]. Here recongurability is modeled as a set of predened congurations (called scenarios), coupled with a nondeterministic nite-state machine that species the transitions between these scenarios.…”

Section: Related Workmentioning

confidence: 99%

RDF: A Reconfigurable Dataflow Model of Computation

Fradet

Girault

Krishnaswamy

et al. 2022

ACM Trans. Embed. Comput. Syst.

View full text Add to dashboard Cite

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“…A preliminary version of this work can be found in [6]. Furthermore, the omitted proofs can be found in the report version of this article [7].…”

Section: Introductionmentioning

confidence: 99%

Parameterized Dataflow Scenarios

Skelin

Geilen

Catthoor

et al. 2017

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-A number of modeling approaches combining dataflow and finite-state machines (FSMs) have been proposed to capture applications that combine streaming data with finite control. FSM-based scenario-aware dataflow (FSM-SADF) is such an FSM/dataflow hybrid that occupies a sweet spot in the tradeoff between analyzability and expressiveness. However, the model suffers from compactness issues when the number of scenarios increases. This hampers its use in analysis of applications exposing high levels of data-dependent dynamics.In this article, we address this problem by combining parameterized dataflow with finite control of FSM-SADF. We refer to the generalization as FSM-based parameterized scenario-aware dataflow (FSM-πSADF). We introduce the formal semantics of the model, in terms of Max-plus algebra and in particular Maxplus automata. Thereafter, by leveraging the existing results of FSM-SADF, we propose a worst-case performance analysis framework for FSM-πSADF. We show that by using FSM-πSADF and its analysis framework, one can, unlike with FSM-SADF, compactly capture streaming applications exhibiting high levels of data-dependent dynamics in presence of finite control. Furthermore, we show that for practical models our analysis typically yields tighter bounds on worst-case performance indicators such as throughput and latency than the existing techniques based on conservative FSM-SADF modeling (if such modeling can be applied at all). We evaluate our approach on a realistic case-study from the multimedia domain.Index Terms-parameterized dataflow, finite-state machine, parameterized dataflow scenarios, synchronous dataflow, Maxplus algebra, worst-case performance.

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