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This study examines the hardware efficiency and model accuracy on three model architectures for classifying ectopic beats on an STM32H743VIT6 microcontroller. The models include two pre-trained CNNs, ResNet V1 and MobileNet V2, and a lightweight CNN named LMUEBCNet. The research evaluates hardware metrics like memory usage, energy consumption, computation time, and accuracy to find an optimal balance. It also discusses redesigning the STM32 chip into an AI module for commercial ECG devices, achieving 99.7% accuracy. The study concludes that while 2D models generally had higher accuracy, the 1D models, particularly MobileNet V2, offered a significant reduction in memory usage and computation time, making them more suitable for resource-limited systems. It indicates that the 1D models, particularly MobileNetV2, exhibited a significant reduction in memory usage and computation time, making them more suitable for resource-limited systems.

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Comparing the Hardware Efficiency and Accuracy of 1D and 2D Convolutional Neural Networks for Ectopic Beat Detection on ARM Embedded Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Cheng-Yang Lee,
  • Chang-Wei Chen,
  • Che-Wei Lin

摘要

This study examines the hardware efficiency and model accuracy on three model architectures for classifying ectopic beats on an STM32H743VIT6 microcontroller. The models include two pre-trained CNNs, ResNet V1 and MobileNet V2, and a lightweight CNN named LMUEBCNet. The research evaluates hardware metrics like memory usage, energy consumption, computation time, and accuracy to find an optimal balance. It also discusses redesigning the STM32 chip into an AI module for commercial ECG devices, achieving 99.7% accuracy. The study concludes that while 2D models generally had higher accuracy, the 1D models, particularly MobileNet V2, offered a significant reduction in memory usage and computation time, making them more suitable for resource-limited systems. It indicates that the 1D models, particularly MobileNetV2, exhibited a significant reduction in memory usage and computation time, making them more suitable for resource-limited systems.