학술논문
ZuSE Ki-Avf: Application-Specific AI Processor for Intelligent Sensor Signal Processing in Autonomous Driving
Document Type
Conference
Author
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.; Kohler, D.; Jauch, A.; Borrmann, J.M.; Jaberansari, S.; Berthold, T.; Blawat, M.; Kock, M.; Schewior, G.; Benndorf, J.; Kautz, F.; Bluethgen, H.-M.; Sauer, C.
Source
2023 Design, Automation & Test in Europe Conference & Exhibition (DATE) Design, Automation & Test in Europe Conference & Exhibition (DATE), 2023. :1-6 Apr, 2023
Subject
Language
ISSN
1558-1101
Abstract
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 cus-tomized 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.