Institute for Communication Technologies and Embedded Systems

ZuSE-KI-AVF: Application-Specific AI Processor for Intelligent Sensor Signal Processing in Autonomous Driving

Thieu, G. B. ,  Gesper, S. ,  Paya Vaya, G. ,  Riggers, C. ,  Renke, O. ,  Fiedler, T. ,  Marten, J. ,  Stuckenberg, T. ,  Blume, H. ,  Weis, C. ,  Steiner, L. ,  Sudarshan, C. ,  Wehn, N. ,  Reimann, L. M.Leupers, R. ,  Beyer, M. ,  Köhler, D. ,  Jauch, A. ,  Bormann, J. M. ,  Jaberansari, S. ,  Berthold, T. ,  Blawat, M. ,  Kock, M. ,  Schewior, G. ,  Benndorf, J. ,  Kautz, F. ,  Bluethgen, H.-M. ,  Sauer, C.
Book Title:
Proceedings of the Conference on Design, Automation & Test in Europe (DATE)
Modern and future AI-based automotive applications, such as autonomous driving, require the efficient real-time processing of huge amounts of data from different sensors, like camera, radar, and LiDAR. In the ZuSE-KI-AVF project, multiple university, and industry partners collaborate to develop a novel massive parallel processor architecture, based on a customized RISC-V host processor, and an efficient high-performance vertical vector coprocessor. In addition, a software development framework is also provided to efficiently program AI-based sensor processing applications. The proposed processor system was verified and evaluated on a state-of-the-art UltraScale+ FPGA board, reaching a processing performance of up to 126.9 FPS, while executing the YOLO-LITE CNN on 224x224 input images. Further optimizations of the FPGA design and the realization of the processor system on a 22nm FDSOI CMOS technology are planned.