Schmidt, Patrick and Topko, Iuliia and Stammler, Matthias and Harbaum, Tanja and Becker, Juergen and Berner, Rico and Ahmed, Omar and Jagielski, Jakub and Seidler, Thomas and Abel, Markus and Kreutzer, Marius and Kirschner, Maximilian and Betancourt, Victor Pazmino and Sehm, Robin and Groth, Lukas and Neskovic, Andrija and Meyer, Rolf and Mulhem, Saleh and Berekovic, Mladen and Probst, Matthias and Brosch, Manuel and Sigl, Georg and Wild, Thomas and Ernst, Matthias and Herkersdorf, Andreas and Aigner, Florian and Hommes, Stefan and Lauer, Sebastian and Seidler, Maximilian and Raste, Thomas and Bozic, Gasper Skvarc and Ceberio, Ibai Irigoyen and Hassan, Muhammad and Mayer, Albrecht     «

Schmidt, Patrick and Topko, Iuliia and Stammler, Matthias and Harbaum, Tanja and Becker, Juergen and Berner, Rico and Ahmed, Omar and Jagielski, Jakub and Seidler, Thomas and Abel, Markus and Kreutzer, Marius and Kirschner, Maximilian and Betancourt, Victor Pazmino and Sehm, Robin and Groth, Lukas and Neskovic, Andrija and Meyer, Rolf and Mulhem, Saleh and Berekovic, Mladen and Probst, Matthias and Brosch, Manuel and Sigl, Georg and Wild, Thomas and Ernst, Matthias and Herkersdorf, Andreas and A...     »

Future automotive architectures are expected to transition from a network-centric to a domain-centered architecture featuring central compute units. Powerful domain controllers or smart sensors alleviate the load on these central units and communication systems. These controllers execute tasks with varying criticalities on heterogeneous multicore processors, and are ideally capable of dynamically balancing the computing load between the central unit and sensors. Here, Artificial Intelligence (AI) capabilities play a crucial role, as it is in high demand for such an automotive architecture. However, AI still requires specialized accelerators to improve their computation performance. Task-oriented distributed computing with criticalities up to ASIL-D necessitates the development and utilization of specialized methodologies, such as safety, through the isolation and abstraction of low-level hardware concepts. Meanwhile, online monitoring and diagnostics become vital features to detect errors during operation. The EMDRIVE architecture includes methods, components, and strategies to enhance the performance, safety, and security of such distributed computing platforms. The nationally funded EMDRIVE project connects its twelve partners from academia and industry and is currently in its intermediate stage.      «

Future automotive architectures are expected to transition from a network-centric to a domain-centered architecture featuring central compute units. Powerful domain controllers or smart sensors alleviate the load on these central units and communication systems. These controllers execute tasks with varying criticalities on heterogeneous multicore processors, and are ideally capable of dynamically balancing the computing load between the central unit and sensors. Here, Artificial Intellige...     »