Multi-Partner Project: CeCaS Accelerator Design for Efficient Supercomputing in Automotive Systems

Modern vehicles integrate an increasing amount of computational functionality, driven by the growing complexity of in-vehicle applications. At the same time, automotive system architectures are becoming more centralized, requiring powerful HPC platforms at the core. These platforms must deliver the performance needed for ADAS, AI, and autonomous driving, while also meeting stringent energy efficiency and safety requirements.The CeCaS project addresses these challenges across a wide range of topics and domains of expertise, including processor design in advanced FinFET technology, the transformation of the E/E architecture, and advanced packaging for automotive supercomputing platforms. Within CeCaS, our work focuses on application-specific accelerator design to enable efficient processing of compute-intensive workloads. In this paper, we present our contributions in this area, including the design of hardware accelerators for both conventional and neuromorphic AI workloads, the development and evaluation of representative AI benchmarks, and the use of virtual platforms for early design-space exploration and hardware/software co-design.