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A Survey on Optimization Techniques for Edge Artificial Intelligence (AI).

Chellammal Surianarayanan1, John Jeyasekaran Lawrence2, Pethuru Raj Chelliah3

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Large Artificial Intelligence (AI) models require significant resources. This study explores AI model optimization techniques to enable efficient deployment on edge devices without performance loss.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning Engineering

Background:

  • Artificial Intelligence (AI) models are increasingly complex and resource-intensive, posing challenges for deployment.
  • The growing need for real-time insights and edge intelligence necessitates efficient AI model processing on devices like IoT.

Purpose of the Study:

  • To comprehensively review and describe various AI model optimization techniques.
  • To highlight the importance of an enabling framework for AI model optimization.

Main Methods:

  • Exploration of AI model optimization at different levels and layers.
  • Analysis of techniques for compressing large AI models.

Main Results:

  • Identification of numerous optimization techniques and tools developed by AI researchers.
  • Demonstration of methods to maximally compress AI models while preserving performance.

Conclusions:

  • AI model optimization is crucial for efficient deployment, especially in edge computing scenarios.
  • An integrated AI model optimization framework is essential for practical implementation and widespread adoption.