Programmable Systems for Intelligence in Automobiles (PRYSTINE)
Themes: Electronic Systems and VLSI Design
PRYSTINE will deliver (a) fail-operational sensor-fusion framework on component level, (b) dependable embedded E/E architectures, and (c) safety compliant integration of Artificial Intelligence (AI) approaches for object recognition, scene understanding, and decision making within automotive applications.TUD will focus on the design of programmable compute hardware to enable automatic driving functions, across two application targets: data fusion for robust perception; and acceleration of AI frameworks for decision making. TUD will investigate the applicability of neuromorphic computing architectures, and programmable hardware fabrics, within PRYSTINE's envelope of objectives: robust perception & dependable embedded control at reduced cost and power-consumption. These efforts will span the flow from architecture exploration, down to system-level design and prototyping, as well as physical design. TUD will carry out the architectural exploration of a hardware compute solution for multi-sensor data fusion. Input data to this hardware can be a set of correlated data frames (images, numerical data,...), and output data is a consolidated, fused data stream that can be used as the basis for decision-making/scenario assessment. The objective of this task is to realize a programmable system that can robustly fuse arbitrary data streams, together with a methodology to customize the solution to multiple design points. Further, TUD will investigate hardware architectures to accelerate AI kernels and decision-making frameworks, in the context of automatic driving functions. This activity could include hardware neural networks realized either as a full-custom/semi-custom VLSI implementation, or within a programmable fabric. Finally, to facilitate efficient and robust multi-sensor data fusion across disparate sources, TUD will investigate the hardware acceleration of communication stacks within the programmable system. Alongside this, TUD will investigate the reliability and dependability of the E/E architectures designed for sensor fusion and decision making. Key topics include aging, device degradation, as well as fault tolerance and mitigation techniques. The hardware platforms developed will be combined with algorithms and application software from PRYSTINE partners for performance validation and demonstration within a lab environment.