ZuSE-KI-Mobil AI Chip Design Platform: An Overview

Shaown Mojumder, Simon Friedrich, Emil Matús, Gerhard P. Fettweis, Matthias Lüders, Martin Friedrich, Oliver Renke, Holger Blume, Julian Höfer, Patrick Schmidt, Jürgen Becker, Darius Grantz, Markus Kock, Jens Benndorf, Nael Fasfous, Pierpaolo Morì, Hans-Jörg Vögel, Samira Ahmadifarsani, Leonidas Kontopoulos, Ulf Schlichtmann et al. (1 additional authors not shown)

Abstract: The ZuSE-KI-Mobil (ZuKIMo) project, a nationally funded initiative, focuses on creating an advanced ecosystem optimized for AI-driven applications in automotive, drone, and industrial domains. At the heart of this effort is a state-of-the-art System-on-Chip (SoC), successfully taped out using 22 nm FDX technology, integrating a novel AI accelerator tailored to specific use case requirements, along with proof-of-concept demonstrators that validate the platform’s real-world application potential. Key aspects include the customized compiler flow, the hardware generation process of the novel AI accelerator, and the acceleration of different applications using the ZuKIMo platform. Examples of these applications are 3D object detection and disengagement prediction in autonomous driving. The paper provides an overview of the ZuKIMo ecosystem, highlighting its contributions to AI performance, energy efficiency, and safety in heterogeneous AI hardware platforms.