ZuSE-KI-Mobil AI Chip Design Platform

An Overview

authored by

Shaown Mojumder, Simon Friedrich, Emil Matus, Gerhard Fettweis, Matthias Lueders, Martin Friedrich, Oliver Renke, Holger Blume, Julian Hoefer, Patrick Schmidt, Juergen Becker, Darius Grantz, Markus Kock, Jens Benndorf, Nael Fasfous, Pierpaolo Mori, Hans Joerg Voegel, Samira Ahmadifarsani, Leonidas Kontopoulos, Ulf Schlichtmann, Kay Bierzynski

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.

Organisation(s)

Architectures and Systems Section

External Organisation(s)

Technische Universität Dresden
Karlsruhe Institute of Technology (KIT)
Dream Chip Technologies GmbH
Bayerische Motoren Werke AG
Technical University of Munich (TUM)
Infineon Technologies AG

Type

Conference contribution

Publication date

29.10.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Hardware and Architecture, Electrical and Electronic Engineering, Instrumentation

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://doi.org/10.1109/NorCAS64408.2024.10752454 (Access: Closed)