logo EDITE Enagnon Cedric KLIKPO
Enagnon Cedric KLIKPO
État académique
Thèse en cours...
Sujet: Méthode de conception de systèmes temps réels embarqués multi-cœurs en milieu automobile
Direction de thèse:
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
Modeling Multi-Periodic Simulink Systems by Synchronous Dataflow Graphs
International audience
The increasing complexity of embedded applications in modern cars has increased the need of computational power. To meet this requirement the European automotive standard AUTOSAR has introduced the use of multi-core platforms in it version 4.x. In the industry, the applications are often designed and validated by high level models such as Matlab/Simulink before being implemented on AUTOSAR. However, passing from a Simulink synchronous model to a multi-core AUTOSAR implementation is not trivial. In this paper, we present an approach to model formally the synchronous semantic of any multi-periodic Simulink system by Synchronous Dataflow Graph. Our model is constructed on a formal equivalence between the data dependencies imposed by the communication mechanisms in Simulink and the precedence constraints of a synchronous dataflow graph. The resulting graph is equivalent in size to the Simulink description and allows multi/many-core accurate implementation analysis.
Real-Time and Embedded Technology and Application Symposium RTAS https://hal.archives-ouvertes.fr/hal-01358061 Real-Time and Embedded Technology and Application Symposium RTAS, Apr 2016, Vienna, Austria. IEEE, pp.1-10, 2016, <10.1109/RTAS.2016.7461343>ARRAY(0x7f03fed67ef8) 2016-04-11
Preemptive scheduling of dependent periodic tasks modeled by synchronous dataflow graphs
International audience
Advanced features in modern cars have increased the criti-cality level of embedded applications in automotive. These applications are generally composed of several communicating functions, for which a deterministic data exchanges is crucial. In the industry, applications are designed with high level models such as Matlab/Simulink. They are implemented on an AUTOSAR platform, where they are scheduled with a fixed-priority based Operating System (OS). However, AUTOSAR OS does not directly provide support for deterministic dataflow implementation. In this paper, we present an approach to implement a deterministic dataflow of dependent periodic tasks on pre-emptive fixed-priority based uniprocessor. We consider a multi-periodic system consisting in several dependent real-time tasks modeled by a Synchronous Dataflow Graph. We use the scheduling of the graph to make the dependent tasks set independent. This permits to insure a deterministic dataflow without requiring synchronization mechanisms. In addition, it allows to use the existing scheduling policies for independent tasks. We propose several heuristics which find a scheduling solution in 76 percent of cases and provide a fast method to deal with dependencies in multi-periodic systems .
Real-Time Networks and Systems RTNS https://hal.archives-ouvertes.fr/hal-01449876 Real-Time Networks and Systems RTNS, Oct 2016, Brest, France. pp.77 - 86, 2016, <10.1145/2997465.2997474>ARRAY(0x7f03fed51710) 2016-10-19
Computing latency of a real-time system modeled by Synchronous Dataflow Graph
International audience
Mixed applications that gather real-time tasks and best effort jobs require a research effort in order to be effectively modeled and executed. Therefore, in this study we define a general and intuitive communication model between multi-periodic real-time tasks. We first demonstrate that the communications between real-time tasks can be directly expressed as a " Synchronous Data-flow Graph ". This model-ing allows precise definition of the system latency. Accordingly , we develop an exact evaluation method to calculate the worst case latency of a system from a given input to a connected outcome. Then, we frame this value using two algorithms that compute its upper and lower bounds. Finally, we show that these bounds can be computed using a polynomial amount of computation time, while the time required to compute the exact value increases linearly according to the average repetition factor. Furthermore, the gap between the exact result and its upper (resp. lower) bound is evaluated between 10 and 15 % (resp. 20 and 30%).
Real-Time Networks and Systems RTNS https://hal.archives-ouvertes.fr/hal-01449892 Real-Time Networks and Systems RTNS, Oct 2016, Brest, France. pp.87 - 96, 2016, <10.1145/2997465.2997479>ARRAY(0x7f03fed5d378) 2016-10-19