论文标题
在低端边缘设备上用于设备学习的顺序概念漂移检测方法
A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices
论文作者
论文摘要
边缘AI系统的一个实际问题是，训练有素的数据集和部署环境的数据分布可能会因噪声和环境变化而随着时间的推移而有所不同。这种现象被称为概念漂移，并且这种差距降低了边缘AI系统的性能，并可能引入系统故障。为了解决这一差距，通过概念漂移检测触发的神经网络模型的重新训练是一种实用方法。但是，由于可用的计算资源在边缘设备中受到严格限制，因此在本文中，我们提出了一种与神经网络的设备顺序学习技术合作的完全顺序的概念漂移检测方法。在这种情况下，神经网络再培训和提出的概念漂移检测仅通过顺序计算来减少计算成本和内存利用。提出方法的评估结果表明，与现有的基于批次的检测方法相比，虽然准确性降低了3.8％-4.3％，但它的记忆尺寸降低了88.9％-96.4％，执行时间降低了1.3％-83.8％。结果，神经网络再培训和提出的概念漂移检测方法的组合在具有264KB内存的Raspberry Pi Pico上证明。
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.
