In this paper, we introduce a low-cost and low-power tiny supervised on-device learning (ODL) core that can address the distributional shift of input data for human activity recognition. Although ODL for resource-limited edge devices has been studied recently, how exactly to provide the training labels to these devices at runtime remains an open-issue. To address this problem, we propose to combine an automatic data pruning with supervised ODL to reduce the number queries needed to acquire predicted labels from a nearby teacher device and thus save power consumption during model retraining. The data pruning threshold is automatically tuned, eliminating a manual threshold tuning. As a tinyML solution at a few mW for the human activity recognition, we design a supervised ODL core that supports our automatic data pruning using a 45nm CMOS process technology. We show that the required memory size for the core is smaller than the same-shaped multilayer perceptron (MLP) and the power consumption is only 3.39mW. Experiments using a human activity recognition dataset show that the proposed automatic data pruning reduces the communication volume by 55.7% and power consumption accordingly with only 0.9% accuracy loss.

翻译：本文提出了一种低成本、低功耗的微型监督式设备端学习核心，能够应对人类活动识别中输入数据的分布偏移。尽管针对资源受限边缘设备的设备端学习已有研究，但如何在运行时准确提供训练标签仍是一个未解决的问题。为解决此难题，我们提出将自动数据剪枝与监督式设备端学习相结合，以减少从邻近教师设备获取预测标签所需的查询次数，从而降低模型重训练阶段的功耗。数据剪枝阈值通过自动调优，无需人工阈值调整。作为面向人类活动识别的毫瓦级微型机器学习解决方案，我们采用45纳米CMOS工艺技术设计了一款支持自动数据剪枝的监督式设备端学习核心。实验表明，该核心所需存储容量小于同结构多层感知机，功耗仅为3.39毫瓦。基于人类活动识别数据集的实验证明，所提出的自动数据剪枝方法在仅损失0.9%准确率的情况下，可降低55.7%的通信量并相应减少功耗。