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Supporting AI-powered real-time cyber-physical systems on heterogeneous platforms via hypervisor technology.

Authors :
Cittadini, Edoardo
Marinoni, Mauro
Biondi, Alessandro
Cicero, Giorgiomaria
Buttazzo, Giorgio
Source :
Real-Time Systems; Dec2023, Vol. 59 Issue 4, p609-635, 27p
Publication Year :
2023

Abstract

The heavy use of machine learning algorithms in safety-critical systems poses serious questions related to safety, security, and predictability issues, requiring novel architectural approaches to guarantee such properties. This paper presents an architecture solution that leverages heterogeneous platforms and virtualization technologies to support AI-powered applications consisting of modules with mixed criticalities and safety requirements. The hypervisor exploits the security features of the Xilinx ZCU104 MPSoCs to create two isolated execution environments: a high performance domain running deep learning algorithms under the Linux operating system and a safety-critical domain running control and monitoring functions under the freeRTOS real-time operating system. The proposed approach is validated by a use case consisting of an unmanned aerial vehicle capable of tracking moving targets using a deep neural network accelerated on the FGPA available on the platform. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09226443
Volume :
59
Issue :
4
Database :
Complementary Index
Journal :
Real-Time Systems
Publication Type :
Academic Journal
Accession number :
174096018
Full Text :
https://doi.org/10.1007/s11241-023-09402-4