License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.NG-RES.2022.1
URN: urn:nbn:de:0030-drops-161099
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16109/
Buttazzo, Giorgio
Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper)
Abstract
The excellent performance of deep neural networks and machine learning algorithms is pushing the industry to adopt such a technology in several application domains, including safety-critical ones, as self-driving vehicles, autonomous robots, and diagnosis support systems for medical applications. However, most of the AI methodologies available today have not been designed to work in safety-critical environments and several issues need to be solved, at different architecture levels, to make them trustworthy. This paper presents some of the major problems existing today in AI-powered embedded systems, highlighting possible solutions and research directions to support them, increasing their security, safety, and time predictability.
BibTeX - Entry
@InProceedings{buttazzo:OASIcs.NG-RES.2022.1,
author = {Buttazzo, Giorgio},
title = {{Can We Trust AI-Powered Real-Time Embedded Systems?}},
booktitle = {Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
pages = {1:1--1:14},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-221-1},
ISSN = {2190-6807},
year = {2022},
volume = {98},
editor = {Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/16109},
URN = {urn:nbn:de:0030-drops-161099},
doi = {10.4230/OASIcs.NG-RES.2022.1},
annote = {Keywords: Real-Time Systems, Heterogeneous architectures, Trustworthy AI, Hypervisors, Deep learning, Adversarial attacks, FPGA acceleration, Mixed criticality systems}
}
Keywords: |
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Real-Time Systems, Heterogeneous architectures, Trustworthy AI, Hypervisors, Deep learning, Adversarial attacks, FPGA acceleration, Mixed criticality systems |
Collection: |
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Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022) |
Issue Date: |
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2022 |
Date of publication: |
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11.06.2022 |