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LiMO:一个轻量级的MambaOut系统,用于端到端的IoMT心电图诊断,在MCU上具有完全可配置的量子化共设计

Tiancheng Cao, Hongtao Li, Hen-Wei Huang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

LiMO是一种轻量级的MambaOut架构,可以在医疗物联网 (IoMT) 的微控制器上实现实时心电图 (ECG) 诊断. 这种节能解决方案可以在最小的参数和资源使用的情况下实现高精度.

科学领域:

人工智能的人工智能
生物医学工程生物医学工程
计算机科学计算机科学

背景情况:

像Mamba这样的深度学习框架提供了效率,但在资源有限的医疗物联网 (IoMT) 边缘设备上面临部署挑战.
对于医疗应用,IoMT设备需要实时处理,安全的数据处理和超低功耗.

研究的目的:

推出LiMO,一种轻量级的MambaOut架构,旨在实现微控制器单元 (MCU) 上的实时心电图 (ECG) 诊断.
解决 IoMT 环境中边缘设备的计算和功率限制.

主要方法:

开发了LiMO,一种完全可配置的量子化,规模线性层融合和硬件软件联合设计方法.
在nRF52840 MCU上部署LiMO模型,评估其参数数量,内存足迹,精度和能耗.

主要成果:

在只有1.8k个参数的MIT-BIH数据集上,LiMO模型实现了98.75%的准确性.
在nRF52840 MCU上部署需要54.4KB的Flash和10.25KB的RAM.
在3.3V时,每次心跳分类的能量消耗低至8.08mJ.

结论:

LiMO成功地弥合了高级深度学习和基于MCU的IoMT要求之间的差距,用于实时ECG诊断.
介绍了在边缘设备上进行下一代医疗监测的实用和节能解决方案.

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