The planned platform is a new development in the field of neuromorphic hardware. This functional principle is inspired by the human brain and specially designed and optimized for the efficient use of neural networks. In particular, it takes into account the fact that product cycles in the automotive sector are very long on the one hand, but that AI algorithms are developing rapidly on the other. The development goal of the project is therefore a hardware platform that can be easily and quickly adapted to new software and hardware requirements in the field of machine learning.
Upon request, Fraunhofer IIS provided details: The hardware will be based on a flexibly programmable multi-core deep learning accelerator in the form of a specially developed chip (ASIC). This will consist of a DLI multi-core processor system optimized for image processing and a flexible DLI accelerator core with embedded reconfigurable logic. The integration of a reprogrammable logic core gives the multi-core deep learning accelerator the flexibility it needs to implement new AI algorithms in the future.
Reconfiguration of the platform is purely software-based. Within the framework of the KI-FLEX project, appropriate methods and tools will be developed to ensure the functional security of the AI algorithms in the application.
Fraunhofer IIS will lead the project consortium, which includes a number of research and industry partners such as the lidar specialist Ibeo Automotive Systems GmbH, the chip manufacturer Infineon, the processor IP developer videantis GmbH, the Chair for Robotics, Artificial Intelligence and Real-Time Systems at the Technical University of Munich, the Fraunhofer Institute for Open Communication Systems FOKUS, the Daimler Center for Automotive IT Innovations (DCAITI) at the Technical University of Berlin and the FAU university of Erlangen-Nuremberg.