The ZuSE-KI-Mobil AI Accelerator SoC: Overview and a Functional Safety Perspective

F. Kempf, Julian Hoefer, T. Harbaum, Juergen Becker, Nael Fasfous, Alexander Frickenstein, Hans-Jörg Vögel, Simon Friedrich, R. Wittig, E. Matús, G. Fettweis, Matthias Lüders, Holger Blume, Jens Benndorf, Darius Grantz, Martin Zeller, Dietmar Engelke, K. Eickel
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引用次数: 1

Abstract

ZuSE-KI-Mobil (ZuKIMo) is a nationally funded research project, currently in its intermediate stage. The goal of the ZuKIMo project is to develop a new System-on-Chip (SoC) platform and corresponding ecosystem to enable efficient Artificial Intelligence (AI) applications with specific requirements. With ZuKIMo, we specifically target applications from the mobility domain, i.e. autonomous vehicles and drones. The initial ecosystem is built by a consortium consisting of seven partners from German academia and industry. We develop the SoC platform and its ecosystem around a novel AI accelerator design. The customizable accelerator is conceived from scratch to fulfill the functional and non-functional requirements derived from the ambitious use cases. A tape-out in 22 nm FDX-technology is planned in 2023. Apart from the System-on-Chip hardware design itself, the ZuKIMo ecosystem has the objective of providing software tooling for easy deployment of new use cases and hardware-CNN co-design. Furthermore, AI accelerators in safety-critical applications like our mobility use cases, necessitate the fulfillment of safety requirements. Therefore, we investigate new design methodologies for fault analysis of Deep Neural Networks (DNNs) and introduce our new redundancy mechanism for AI accelerators.
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ZuSE-KI-Mobil AI加速器SoC:概述和功能安全视角
ZuSE-KI-Mobil (ZuKIMo)是一项国家资助的研究项目,目前处于中期阶段。ZuKIMo项目的目标是开发一种新的片上系统(SoC)平台和相应的生态系统,以实现具有特定需求的高效人工智能(AI)应用。通过ZuKIMo,我们专门针对移动领域的应用,即自动驾驶汽车和无人机。最初的生态系统是由来自德国学术界和工业界的七个合作伙伴组成的联盟建立的。我们围绕新颖的AI加速器设计开发SoC平台及其生态系统。可定制的加速器是从头开始构思的,以满足从雄心勃勃的用例派生的功能和非功能需求。计划在2023年完成22纳米fdx技术的生产。除了片上系统硬件设计本身,ZuKIMo生态系统的目标是提供软件工具,以便轻松部署新的用例和硬件- cnn协同设计。此外,在像我们的移动用例这样的安全关键应用中,人工智能加速器需要满足安全要求。因此,我们研究了深度神经网络(dnn)故障分析的新设计方法,并为人工智能加速器引入了新的冗余机制。
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