Heterogeneous Real-time systems Implementability for AI Tasks on Accelerators and GPU-based Embedded architectureS – HeRITAGES

During the last decade, artificial intelligence (AI) has received particular attention from academic and industrial communities. This made it possible to develop complex systems, such as those required for Autonomous driving and Advanced Driver-Assistance Systems (ADAS). These systems process a large amount of data, on which a series of AI inferences are applied. Typical research interests in the AI community focus on improving recognition rates, without considering AI inference execution constraints. In this project, we want to improve both predictability and efficiency of running AI deep learning (DL) inferences, by enabling their timing-aware implementations, and exploiting these implementations with novel scheduling policies, onto complex high-performance heterogeneous embedded computing platforms. The HeRITAGES project will achieve these goals by (i) evaluating timing performances of running DL implementations on different compute elements, to characterize and extract their execution features, (ii) modeling complex DL Inference workloads, composed of dependant DL inferences with the HPC-DAG task model; (iii) studying the impact of the extracted features such as task memory footprint and inter-task interference on resources co-scheduling and (iv) finally, designing novel scheduling frameworks to support the proposed approaches in HeRITAGES project.