Sie verwenden einen Browser, in dem JavaScript deaktiviert ist. Dadurch wird verhindert, dass Sie die volle Funktionalität dieser Webseite nutzen können. Zur Navigation müssen Sie daher die Sitemap nutzen.

You are currently using a browser with deactivated JavaScript. There you can't use all the features of this website. In order to navigate the site, please use the Sitemap .

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

Authors:: 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)
Date:: 2023
Language:: English
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 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.
Download:: BibTeX