Méthodologies et outils de portage d’algorithmes de traitement d’images sur cibles hardware mixte

Car manufacturers increasingly provide Advanced Driver Assistance Systems (ADAS) based on cameras and image processing algorithms. To embed ADAS applications, semiconductor companies propose heterogeneous architectures. These Systems-on-Chip (SoCs) are composed of several processors with different capabilities on the same chip. However, with the increasing complexity of such systems, it becomes more and more difficult for an automotive actor to chose a SoC which can execute a given ADAS application while meeting real-time constraints. In addition, embedding algorithms on this type of hardware is not trivial: one needs to determine how to spread the computational load between the different processors, in others words the mapping of the computational load.In response to this issue, we defined during this thesis a global methodology to study the embeddability of image processing algorithms for real-time execution. This methodology predicts the embeddability of a given image processing algorithm on several heterogeneous SoCs by automatically exploring the possible mapping. It is based on three major contributions: the modeling of an algorithm and its real-time constraints, the characterization of a heterogeneous SoC, and a performance prediction approach which can address different types of architectures.