Abstract
This work presents MicroNAS, an automated neural architecture search tool specifically designed to create models optimized for microcontrollers with small memory resources. The ESP32 microcontroller, with 320 KB of memory, is used as the target platform. The artificial intelligence contribution lies in a novel method for optimizing convolutional neural network and gated recurrent unit architectures by considering the memory size of the target microcontroller as a guide. A comparison is made between memory-driven model optimization and traditional two-stage methods, which use pruning, to show the effectiveness of the proposed framework. To demonstrate the engineering application of MicroNAS, a fall detection system (FDS) for lower-limb amputees is developed as a pilot study. A critical challenge in fall detection studies, class imbalance in the dataset, is addressed. The results show that MicroNAS models achieved higher F1-scores than alternative approaches, such as ensemble methods and H2O Automated Machine Learning, presenting a significant step forward in real-time FDS development. Biomechanists using body-worn sensors for activity detection can adopt the open-source code to design machine learning models tailored for microcontroller platforms with limited memory.
Abstract (translated)
这项工作介绍了MicroNAS，这是一种自动神经架构搜索工具，专门设计用于为内存资源有限的微控制器创建优化模型。目标平台是ESP32微控制器，其内存容量为320 KB。人工智能方面的贡献在于提出了一种新颖的方法，通过考虑目标微控制器的内存大小来优化卷积神经网络和门控循环单元架构。研究将基于内存驱动的模型优化方法与传统的两阶段方法（使用剪枝技术）进行了比较，以展示所提框架的有效性。为了展示MicroNAS的实际工程应用价值，开发了一个用于下肢截肢者的跌倒检测系统(FDS)作为试点研究项目。在这一过程中，还解决了跌倒检测研究中数据集不平衡的关键挑战。结果显示，MicroNAS模型的F1分数高于包括集成方法和H2O自动化机器学习在内的其他方法，这表明在实时FDS开发方面取得了重大进展。从事生物力学研究并使用穿戴式传感器进行活动检测的研究人员可以采用开源代码来设计针对内存受限微控制器平台的机器学习模型。
URL
https://arxiv.org/abs/2504.07397
