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Model and Method for Providing Resilience to Resource-Constrained AI-System.

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Summary
This summary is machine-generated.

This study introduces dynamic neural networks to boost the resilience of artificial intelligence (AI) systems in resource-limited environments. The new method enhances AI robustness against faults and attacks while significantly cutting computational costs.

Keywords:
adversarial attackaffordable resilienceconcept driftdynamic deep neural networksfault injectionrobustness

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial intelligence (AI) is increasingly used in resource-constrained, safety-critical embedded systems.
  • Enhancing AI resilience against disruptions is vital, especially when computational resources are limited.
  • Dynamic neural networks offer potential for reduced resource consumption but their resilience is underexplored.

Purpose of the Study:

  • To propose a novel model architecture and training method integrating dynamic neural networks for enhanced resilience.
  • To evaluate the effectiveness of the proposed approach in improving AI system robustness against various disturbances.
  • To assess the resource efficiency of the new method compared to conventional techniques.

Main Methods:

  • Development of a model architecture that integrates dynamic neural networks with a focus on resilience.
  • Implementation of a training method designed to enhance robustness against fault injections and adversarial attacks.
  • Utilizing meta-training to improve resilience to task changes.

Main Results:

  • A 24% increase in convolutional network resilience and a 19.7% increase in visual transformer resilience under fault injections.
  • A 16.9% increase in ResNet-110 resilience and a 21.6% increase in DeiT-S resilience under adversarial attacks.
  • Over 30% savings in computational resources and an average 22% improvement in resilience to task changes via meta-training.

Conclusions:

  • The proposed integration of dynamic neural networks significantly enhances AI resilience in resource-constrained systems.
  • The method provides substantial improvements against fault injections, adversarial attacks, and task changes.
  • This approach offers a cost-effective solution for deploying robust AI in safety-critical embedded applications.