A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and this gap degrades the performance of edge AI systems and may introduce system failures. To address this gap, retraining of neural network models triggered by concept drift detection is a practical approach. However, since available compute resources are strictly limited in edge devices, in this paper we propose a fully sequential concept drift detection method in cooperation with an on-device sequential learning technique of neural networks. In this case, both the neural network retraining and the proposed concept drift detection are done only by sequential computation to reduce computation cost and memory utilization. Evaluation results of the proposed approach shows that while the accuracy is decreased by 3.8%-4.3% compared to existing batch-based detection methods, it decreases the memory size by 88.9%-96.4% and the execution time by 1.3%-83.8%. As a result, the combination of the neural network retraining and the proposed concept drift detection method is demonstrated on Raspberry Pi Pico that has 264kB memory.

翻译：边缘AI系统的实际问题是，由于噪声和随时间变化的环境因素，训练数据集与部署环境的数据分布可能产生差异。这种现象被称为概念漂移，这种差异会降低边缘AI系统的性能，甚至引发系统故障。为解决该差异，基于概念漂移检测触发神经网络模型重训练是可行的方案。然而，由于边缘设备的计算资源严格受限，本文提出一种与神经网络设备端序列化学习技术协同的全序列化概念漂移检测方法。在此方案中，神经网络重训练与所提出的概念漂移检测均仅通过序列化计算实现，以降低计算开销和内存占用。评估结果表明，与现有基于批处理的检测方法相比，所提方法的准确率下降3.8%-4.3%，但内存占用减少88.9%-96.4%，执行时间缩短1.3%-83.8%。最终，该神经网络重训练与概念漂移检测方法的组合方案已在配备264kB内存的Raspberry Pi Pico上完成验证。