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Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper)

Author Giorgio Buttazzo

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ACM Subject Classification
  • Computer systems organization
Keywords
  • Real-Time Systems
  • Heterogeneous architectures
  • Trustworthy AI
  • Hypervisors
  • Deep learning
  • Adversarial attacks
  • FPGA acceleration
  • Mixed criticality systems

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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.

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Giorgio Buttazzo. Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper). In Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 1:1-1:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/OASIcs.NG-RES.2022.1

Author Details

Giorgio Buttazzo
  • Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy

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