ZuSE KI-AVF

Application-Specific AI Processor for Intelligent Sensor Signal Processing in Autonomous Driving

verfasst von

Gia Bao Thieu, Sven Gesper, Guillermo Payá-Vayá, Christoph Riggers, Oliver Renke, Till Niklas Fiedler, Jakob Marten, Tobias Stuckenberg, Holger Blume, Christian Weis, Lukas Steinert, Chirag Sudarshan, Norbert Wehn, Lennart M. Reimann, Rainer Leupers, Michael Beyer, Daniel Köhler, Alisa Jauch, Jan Micha Borrmann, Setareh Jaberansari, Tim Berthold, Meinolf Blawat, Markus Kock, Gregor Schewior, Jens Benndorf, Frederik Kautz, Hans-Martin Bluethgen, Christian Sauer

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.

Organisationseinheit(en)

Institut für Mikroelektronische Systeme

Externe Organisation(en)

Rheinisch-Westfälische Technische Hochschule Aachen (RWTH)
Robert Bosch GmbH
Dream Chip Technologies GmbH
Cadence Design Systems
Technische Universität Braunschweig
Technische Universität Kaiserslautern

Typ

Aufsatz in Konferenzband

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Ingenieurwesen (insg.)

Elektronische Version(en)

https://doi.org/10.23919/date56975.2023.10136978 (Zugang: Geschlossen)