In the signal processing domain, dataflow graphs [2] [10] and their associated analysis techniques are a well-accepted modeling paradigm. The vertices of a dataflow graph represent functionality and are called actors, while the edges model which actors communicate with each other. Traditionally, these actors have been scheduled in a static order or in a fully static fashion [9] [16]. However, for firm real-time applications, run-time scheduling is required if there are tasks, that operate on different streams and share the same resource, for which either the execution time or the execution rate is unknown or impractical to bound
This paper describes and analyzes a paradigm for scheduling com-putations on a network of multiproce...
In many signal processing applications, the tokens in a stream of tokens have a dimension higher tha...
International audienceProcess Networks are a means to describe streaming embedded applications. They...
In order to obtain a cost-efficient solution, tasks share resources in a Multi-Processor System-on-C...
International audienceWe present the symbolic computation of data-flow graphs latency, with two vari...
Abstract—Dataflow-based application specifications are widely used in model-based design methodologi...
This paper builds upon research by Lee [1] concerning the token flow model, an analytical model for ...
Modern embedded multi-processors can execute several stream-processing applications concurrently. Ty...
Constituent tasks of modern day Embedded Streaming Applications (ESAs), such as engine control syste...
International audienceMixed applications that gather real-time tasks and best effort jobs require a ...
The central thesis of this project is that real-time scheduling theory can be combined with dataflow...
International audienceStatic dataflow graphs are widely used to model concurrent real-time streaming...
Synchronous dataflow graphs (SDFGs) are used extensively to model streaming applications. An SDFG ca...
Dataflow is a natural way of modelling streaming applications, such as multimedia, networking and ot...
Dataflow computers provide a platform for the solution of a large class of computational problems, w...
This paper describes and analyzes a paradigm for scheduling com-putations on a network of multiproce...
In many signal processing applications, the tokens in a stream of tokens have a dimension higher tha...
International audienceProcess Networks are a means to describe streaming embedded applications. They...
In order to obtain a cost-efficient solution, tasks share resources in a Multi-Processor System-on-C...
International audienceWe present the symbolic computation of data-flow graphs latency, with two vari...
Abstract—Dataflow-based application specifications are widely used in model-based design methodologi...
This paper builds upon research by Lee [1] concerning the token flow model, an analytical model for ...
Modern embedded multi-processors can execute several stream-processing applications concurrently. Ty...
Constituent tasks of modern day Embedded Streaming Applications (ESAs), such as engine control syste...
International audienceMixed applications that gather real-time tasks and best effort jobs require a ...
The central thesis of this project is that real-time scheduling theory can be combined with dataflow...
International audienceStatic dataflow graphs are widely used to model concurrent real-time streaming...
Synchronous dataflow graphs (SDFGs) are used extensively to model streaming applications. An SDFG ca...
Dataflow is a natural way of modelling streaming applications, such as multimedia, networking and ot...
Dataflow computers provide a platform for the solution of a large class of computational problems, w...
This paper describes and analyzes a paradigm for scheduling com-putations on a network of multiproce...
In many signal processing applications, the tokens in a stream of tokens have a dimension higher tha...
International audienceProcess Networks are a means to describe streaming embedded applications. They...