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A Method of Deep Learning Model Optimization for Image Classification on Edge Device.

Hyungkeuk Lee1, NamKyung Lee1, Sungjin Lee2

  • 1Media Intelligence Research Section, Electronics and Telecommunications Research Institute, 218, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea.

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|October 14, 2022
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Summary
This summary is machine-generated.

Deep Learning Model Optimization (DLMO) strategies are crucial for edge devices. This study evaluates techniques like quantization and pruning to efficiently deploy AI on IoT devices with minimal accuracy loss.

Keywords:
convolutional neural networkimage classificationknowledge distillationlightweight networknetwork compressionpruningquantization

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Deep learning models are increasingly deployed on edge devices.
  • This trend drives demand for efficient Deep Learning Model Optimization (DLMO) techniques.
  • Optimizing models is essential for resource-constrained environments like IoT.

Purpose of the Study:

  • To derive optimal DLMO strategies for edge devices.
  • To evaluate performance trade-offs of various optimization techniques.
  • To propose suitable DLMO solutions for on-device AI services.

Main Methods:

  • Performance evaluation of light convolution, quantization, pruning, and knowledge distillation.
  • Experimental analysis using image classification tasks.
  • Comparative study of DLMO techniques for minimal accuracy drop and reduced latency.

Main Results:

  • Identified effective DLMO techniques for reducing memory footprint and operation delay.
  • Derived optimal strategies for applying deep learning to IoT and embedded devices.
  • Demonstrated minimal accuracy degradation with applied optimization methods.

Conclusions:

  • Mature deep learning methodologies can provide rational algorithmic solutions for limited resource environments.
  • Specific DLMO strategies are proposed based on performance factors for different on-device AI services.
  • The study offers practical guidance for deploying AI on edge computing platforms.