SPDF: A Schedulable Parametric Data-Flow MoC

Abstract : Dataflow programming models are suitable to express multi-core streaming applications. The design of high- quality embedded systems in that context requires static analysis to ensure the liveness and bounded memory of the application. However, many streaming applications have a dynamic behavior. The previously proposed dataflow models for dynamic applications do not provide any static guarantees or only in exchange of significant restrictions in expressive power or automation. To overcome these restrictions, we propose the schedulable parametric dataflow (SPDF) model. We present static analyses and a quasi-static scheduling algorithm. We demonstrate our approach using a video decoder case study.
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Communication dans un congrès
Design Automation and Test in Europe, DATE'12, Mar 2012, Dresden, Germany. 2012
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Pascal Fradet, Alain Girault, Petro Poplavko. SPDF: A Schedulable Parametric Data-Flow MoC. Design Automation and Test in Europe, DATE'12, Mar 2012, Dresden, Germany. 2012. 〈hal-00744376〉

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