设计和实施一个LSTM模型,并嵌入到MCU上,用于预测气象变量
Jhan Piero Paulo Merma Yucra1, David Juan Cerezo Quina1, German Alberto Echaiz Espinoza1
1Professional School of Electronic Engineering, Universidad Nacional de San Agustín de Arequipa, Arequipa 04002, Peru.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
我们介绍了SE-LSTM,这是一种用于在资源有限的系统上进行时间序列预测的新型循环神经网络变体. 该模型优化了网络大小并保持了性能,这对于农业监控等边缘计算应用至关重要.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 边缘计算 边缘计算
背景情况:
- 循环神经网络 (RNN) 在时间序列预测方面表现出色,但在资源受限的微控制器 (MCU) 上面临尺寸和稳定性的挑战.
- 现有的模型在需要长时间预测窗口的任务中,在ESP32或Raspberry Pi Pico等边缘设备上有效部署方面扎.
研究的目的:
- 提出和评估SE-LSTM (单嵌入式LSTM),这是一个新的LSTM变体,旨在在MCU上高效地预测时间序列.
- 为了评估SE-LSTM的性能,网络重量,内存使用量和能源消耗,与已有的架构相比.
主要方法:
- 开发了SE-LSTM,结合了嵌入技术,以矢量表示季节性和潜在模式.
- 对CNN-LSTM,TCN,LMU和TPA-LSTM架构进行了系统的比较.
- 评估模型使用平均绝对误差 (MAE) 和平均平方误差 (MSE) 度量,以及网络重量,内存和能源消耗分析.
主要成果:
- 在不影响预测性能的情况下,SE-LSTM展示了优化的网络空间.
- 该模型显示了在诸如ESP32和Raspberry Pi Pico之类的MCU上的高效实现.
- 分析包括了不同地理区域的概括能力.
结论:
- 在SE-LSTM中嵌入技术有效地减少模型大小,使其适合边缘计算.
- 在资源有限的环境中,SE-LSTM为时间序列预测提供了可行的解决方案,特别是用于农业监测系统.
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