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An Adaptive Compression Method for Lightweight AI Models of Edge Nodes in Customized Production.

Chun Jiang1,2,3, Mingxin Hou1,3, Hongxuan Wang4

  • 1School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive AI model compression method for edge devices in manufacturing. It enhances model efficiency and accuracy for dynamic production tasks, outperforming static methods.

Keywords:
RLadaptive optimizationcustomized production lineedge intelligencemodel compression

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Area of Science:

  • Edge computing
  • Artificial intelligence
  • Manufacturing systems

Background:

  • Edge AI models struggle with frequent task changes and limited hardware in customized production.
  • Traditional static compression methods lack adaptability and flexibility for dynamic edge environments.

Purpose of the Study:

  • To develop an adaptive lightweight AI model compression method for edge nodes in customized production lines.
  • To address challenges of limited transferability and insufficient flexibility in current edge AI applications.

Main Methods:

  • Constructed a task requirement analysis model based on accuracy, latency, and power consumption.
  • Characterized edge node hardware information and established a compression-strategy candidate pool.
  • Introduced an adaptive decision engine using ensemble reinforcement learning (RL) and Bayesian optimization (BO) with iterative optimization.

Main Results:

  • Generated lightweight models significantly improved inference efficiency while maintaining high accuracy in object-recognition tasks.
  • The proposed method outperformed conventional fixed compression strategies.
  • Validated effectiveness in adaptive capability and edge-deployment performance.

Conclusions:

  • The adaptive lightweight AI model compression method enhances edge AI performance in dynamic production environments.
  • The approach offers superior adaptability and deployment efficiency compared to static methods.
  • This method is crucial for stable operation in customized, multi-task parallel production lines.