Università degli studi di Modena e Reggio Emilia

Modern Cyber Physical Systems (CPS) applications require hardware capable of optimized performance-per-watt efficency. This is usually obtained through massively parallel accelerators such as the GPU. Recent research is therefore investigating novel designs to optimize GPU energy consumption and performance for various applications in the Internet-of-things, autonomous navigation, and industrial robotics domains. This paper presents a survey of the current state-of-the-art approaches for optimizing GPU performance metrics; we present a complete and up-to-date summary of ideas, mechanisms, and potential improvements for next-generation GPU devices.

In this paper we report our experiences in designing and implementing a digital virtual cockpit to be installed as a component within the software stack of an Advanced Driving Assisted System (ADAS). Since in next-generation automotive embedded platforms both autonomous driving related workloads and virtual cockpit rendering tasks will co-run in a hypervisor-mediated environment, they will share computational resources. For this purpose, our work has been developed by following a requirement-driven approach in which regulations, usability and visual attractiveness requirements have to be taken into account by balancing their impact in terms of computational resources of the embedded platform in which such graphics interfaces are deployed. The graphic interfaces we realized consist of a set of 2D frames for the instrument cluster (for displaying the tachometer and the speedometer) and a screen area in which a 3D representation of the vehicle surroundings is rendered alongside driving directions and the point-cloud obtained through a LIDAR. All these components are able to alert the driver of imminent and/or nearby driving hazards.