在物联网网关上自动设计边缘神经网络，保护隐私且高效。
Searching Neural Architectures for Sensor Nodes on IoT Gateways
- 在网关本地搜索定制化神经网络，不外传传感器数据。
- 在树莓派零2上10小时内完成搜索，在视觉警报数据集上达顶尖性能。
- 适合医疗与工业物联网等需隐私保护的场景。
本文提出一种在边缘自动设计神经网络的方法，使机器学习能在隐私敏感的物联网应用中使用。该方法运行于物联网网关，为连接的传感器节点设计神经网络，无需将采集数据传出本地网络，确保数据保留在采集地。该方法有望推动医疗物联网（HIoT）和工业物联网（IIoT）的发展，实现针对个性化医疗和先进工业服务（如质量控制、预测性维护或故障诊断）的硬件友好型定制神经网络。通过防止数据向云端披露，有效保护包括工业机密和个人数据在内的敏感信息。大量实验表明，该方法在视觉警报（Visual Wake Words）数据集上可达到当前最优结果，其搜索过程在树莓派零2上耗时少于10小时。
原文摘要 · Abstract (English)
This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications. The proposed method runs on IoT gateways and designs NNs for connected sensor nodes without sharing the collected data outside the local network, keeping the data in the site of collection. This approach has the potential to enable ML for Healthcare Internet of Things (HIoT) and Industrial Internet of Things (IIoT), designing hardware-friendly and custom NNs at the edge for personalized healthcare and advanced industrial services such as quality control, predictive maintenance, or fault diagnosis. By preventing data from being disclosed to cloud services, this method safeguards sensitive information, including industrial secrets and personal data. The outcomes of a thorough experimental session confirm that -- on the Visual Wake Words dataset -- the proposed approach can achieve state-of-the-art results by exploiting a search procedure that runs in less than 10 hours on the Raspberry Pi Zero 2.
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