TY - GEN
T1 - ZuSE-KI-Mobil AI Chip Design Platform
T2 - 10th IEEE Nordic Circuits and Systems Conference, NORCAS 2024
AU - Mojumder, Shaown
AU - Friedrich, Simon
AU - Matus, Emil
AU - Fettweis, Gerhard
AU - Lueders, Matthias
AU - Friedrich, Martin
AU - Renke, Oliver
AU - Blume, Holger
AU - Hoefer, Julian
AU - Schmidt, Patrick
AU - Becker, Juergen
AU - Grantz, Darius
AU - Kock, Markus
AU - Benndorf, Jens
AU - Fasfous, Nael
AU - Mori, Pierpaolo
AU - Voegel, Hans Joerg
AU - Ahmadifarsani, Samira
AU - Kontopoulos, Leonidas
AU - Schlichtmann, Ulf
AU - Bierzynski, Kay
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - AI Accelerator
KW - Compiler
KW - System-on-Chip
UR - http://www.scopus.com/inward/record.url?scp=85211924264&partnerID=8YFLogxK
U2 - 10.1109/NorCAS64408.2024.10752454
DO - 10.1109/NorCAS64408.2024.10752454
M3 - Conference contribution
AN - SCOPUS:85211924264
T3 - 2024 IEEE Nordic Circuits and Systems Conference, NORCAS 2024 - Proceedings
BT - 2024 IEEE Nordic Circuits and Systems Conference, NORCAS 2024 - Proceedings
A2 - Nurmi, Jari
A2 - Rodrigues, Joachim
A2 - Pezzarossa, Luca
A2 - Aberg, Viktor
A2 - Behmanesh, Baktash
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 October 2024 through 30 October 2024
ER -
