Tools and methods for Edge-AI-systems

被引：0

作者：

Schwabe, Nils ^{[1
]}

Zhou, Yexu ^{[2
]}

Hielscher, Leon ^{[1
]}

Roeddiger, Tobias ^{[2
]}

Riedel, Till ^{[2
]}

Reiter, Sebastian ^{[1
]}

机构：

[1] FZI Res Ctr Informat Technol, Karlsruhe, Germany

[2] Karlsruher Inst Technol KIT, Inst Telemat, Karlsruhe, Germany

来源：

AT-AUTOMATISIERUNGSTECHNIK | 2022年 / 70卷 / 09期

关键词：

Edge-AI; machine learning; hardware acceleration; co-design; auto-ml;

D O I：

10.1515/auto-2022-0023

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The enormous potential of artificial intelligence, especially artificial neural networks, when used for edge computing applications in cars, traffic lights or smart watches, has not yet been fully exploited today. The reasons for this are the computing, energy and memory requirements of modern neural networks, which typically cannot be met by embedded devices. This article provides a detailed summary of today's challenges and gives a deeper insight into existing solutions that enable neural network performance with modern HW/SW co-design techniques.

引用

页码：767 / 776

页数：10

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