Embedded GPU implementation of sensor correction for on-board real-time stream computing of high-resolution optical satellite imagery

Advanced driver assistance systems applications increasingly use cameras and image processing algorithms. To embed and achieve real-time execution of these algorithms, semiconductor companies propose heterogeneous systems-on-chip (SoCs). Embedding algorithms on this type of hardware is not trivial: One needs to determine how to partition the computational load on the different processing units. In addition, it is not easy to predict whether a given algorithm can be executed on a given heterogeneous SoC while meeting real-time constraints. We propose a novel global methodology to assist with embedding image processing algorithms on heterogeneous SoC while meeting real-time constraints (using a soft real-time analysis). Our approach proposes several heuristics predicting delays and execution times and is based on a set of multi-level test vectors which extract key features of heterogeneous architectures.