论文标题
通过设备学习来解决培训数据和部署环境之间的差距
Addressing Gap between Training Data and Deployed Environment by On-Device Learning
论文作者
论文摘要
Tinyml应用的准确性通常会受到各种环境因素的影响，例如噪声，传感器的位置/校准以及与时间相关的变化。本文介绍了基于设备学习（ODL）方法的神经网络，以通过在部署的环境中进行重新培训来解决此问题。我们的方法依赖于针对低端边缘设备量身定制的多个神经网络的半监督顺序训练。本文介绍了由Raspberry Pi Pico和低功率无线模块组成的无线传感器节点上的算法和实现。使用旋转机的振动模式的实验表明，与仅预测的仅预测深神经网络相比，ODL的重新检测可以提高异常检测精度。结果还表明，ODL方法可以节省电池互联网设备的通信成本和能耗。
The accuracy of tinyML applications is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device learning (ODL) approach to address this issue by retraining in deployed environments. Our approach relies on semi-supervised sequential training of multiple neural networks tailored for low-end edge devices. This article introduces its algorithm and implementation on wireless sensor nodes consisting of a Raspberry Pi Pico and low-power wireless module. Experiments using vibration patterns of rotating machines demonstrate that retraining by ODL improves anomaly detection accuracy compared with a prediction-only deep neural network in a noisy environment. The results also show that the ODL approach can save communication cost and energy consumption for battery-powered Internet of Things devices.
