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<p>The rapid advancement of edge artificial intelligence (AI) has unlocked transformative applications across various domains. However, it also poses significant challenges in efficiently updating models on edge devices, which are often constrained by limited computational and communication resources. Here, we present low-rank adaptation method for Edge AI (LoRAE), Leveraging low-rank decomposition of convolutional neural networks (CNNs) weight matrices, LoRAE reduces the number of updated parameters to approximately 4% of traditional full-parameter updates, effectively mitigating the computational and communication challenges associated with model updates. Extensive experiments across image classification, object detection, and image segmentation tasks demonstrate that LoRAE significantly decreases the scale of trainable parameters while maintaining or even enhancing model accuracy. Using the YOLOv8x model, LoRAE achieves parameter reductions of 86.1%, 98.6%, and 94.1% across the three tasks, respectively, without compromising accuracy. These findings highlight the potential of LoRAE as an efficient and precise solution for resource-constrained edge AI systems.</p>

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Low-rank adaptation for edge AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

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  • Zhixue Wang,
  • Hongyao Ma,
  • Jiahui Zhai

摘要

The rapid advancement of edge artificial intelligence (AI) has unlocked transformative applications across various domains. However, it also poses significant challenges in efficiently updating models on edge devices, which are often constrained by limited computational and communication resources. Here, we present low-rank adaptation method for Edge AI (LoRAE), Leveraging low-rank decomposition of convolutional neural networks (CNNs) weight matrices, LoRAE reduces the number of updated parameters to approximately 4% of traditional full-parameter updates, effectively mitigating the computational and communication challenges associated with model updates. Extensive experiments across image classification, object detection, and image segmentation tasks demonstrate that LoRAE significantly decreases the scale of trainable parameters while maintaining or even enhancing model accuracy. Using the YOLOv8x model, LoRAE achieves parameter reductions of 86.1%, 98.6%, and 94.1% across the three tasks, respectively, without compromising accuracy. These findings highlight the potential of LoRAE as an efficient and precise solution for resource-constrained edge AI systems.