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Comparison of Input-Data Matrix Representations Used for Continual Learning with Orthogonal Weight Modification on

Ronald Mendez1, Andreas Maier2, Johannes Emmert1

  • 1Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Flugplatzstr. 75, 90768 Fürth, Germany.

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Summary
This summary is machine-generated.

Artificial Neural Twins (ANT) combined with Orthogonal Weight Modification (OWM) enable autonomous learning in smart industrial devices. The Fisher matrix offers an efficient solution for large AI models, while NEig-OWM suits smaller devices needing more control.

Keywords:
Internet of ThingsMobile Edge Computingartificial neural twincontinual learningdistributed learningorthogonal weight modification

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Area of Science:

  • Artificial Intelligence
  • Industrial Internet of Things (IIoT)
  • Machine Learning

Background:

  • Industrial processes increasingly use smart devices for automation and optimization.
  • Industrial Internet of Things (IIoT) facilitates device communication but lacks advanced process optimization.
  • Object detection sensors are key components in smart industrial applications.

Purpose of the Study:

  • To explore Artificial Neural Twin (ANT) as a distributed optimization tool for industrial processes.
  • To investigate the integration of continual learning (CL) methods like Orthogonal Weight Modification (OWM) for autonomous device learning.
  • To compare matrix approximation methods for reducing computational complexity in CL algorithms on resource-constrained devices.

Main Methods:

  • Utilized an object detection sensor as a testbed for ANT and OWM.
  • Implemented and compared Fisher matrix, NEig-OWM, and LoRA for matrix approximation in CL.
  • Evaluated the trade-offs between computational cost, hardware requirements, and model performance.

Main Results:

  • The Fisher matrix proved to be the most computationally inexpensive approximation for CL.
  • Negligible performance reduction was observed when using the Fisher matrix for CL in large AI models.
  • NEig-OWM demonstrated suitability for smaller models requiring greater control over the CL process.

Conclusions:

  • The Fisher matrix is a viable and cost-effective solution for enabling continual learning in large-scale industrial AI systems.
  • NEig-OWM offers a more controlled approach to continual learning for resource-limited microcontrollers.
  • ANT combined with efficient CL matrix approximations can significantly advance autonomous process optimization in IIoT environments.