SensiX：边缘协作机器学习平台
SensiX: A Platform for Collaborative Machine Learning on the Edge
摘要：
在人体上或附近出现的多个传感设备正在揭示极端边缘计算的新动态。在这种情况下，智能手机或Wi-Fi网关等功能强大且资源丰富的边缘设备将转变为个人边缘设备，与多个设备协作以提供卓越的感官应用，同时利用位置、可用性和邻近性的力量。自然，这种转变促使我们重新思考如何在个人边缘构建准确，强大和有效的感觉系统。例如，我们如何使用多个配备IMU的身体设备构建可靠的活动跟踪器？虽然传感模型的准确性正在提高，但其运行时性能仍然受到影响，特别是在这种新兴的多设备个人边缘环境下。影响其性能的两个主要注意事项是设备和数据可变性，这是由几个运行时因素造成的，包括设备可用性、数据质量和设备放置。为此，我们提出了SensiX，这是一个个人边缘平台，它位于传感器数据和传感模型之间，并确保在任何条件下尽最大努力进行推理，同时应对设备和数据的变化，而无需模型工程。SensiX使模型执行远离应用程序，并包括两个基本功能，一个用于设备到设备数据的原则性映射的转换操作符和一个质量感知选择操作符，以系统地选择正确的执行路径作为模型准确性的函数。我们报告了SensiX的设计和实现，并展示了其在开发基于运动和音频的多设备传感系统方面的功效。我们的评估表明，SensiX的整体精度提高了7-13%，在不同的环境动态下提高了30%，但功耗仅为3 mW。
英文摘要：
The emergence of multiple sensory devices on or near a human body is uncovering new dynamics of extreme edge computing. In this, a powerful and resource-rich edge device such as a smartphone or a Wi-Fi gateway is transformed into a personal edge, collaborating with multiple devices to offer remarkable sensory al eapplications, while harnessing the power of locality, availability, and proximity. Naturally, this transformation pushes us to rethink how to construct accurate, robust, and efficient sensory systems at personal edge. For instance, how do we build a reliable activity tracker with multiple on-body IMU-equipped devices? While the accuracy of sensing models is improving, their runtime performance still suffers, especially under this emerging multi-device, personal edge environments. Two prime caveats that impact their performance are device and data variabilities, contributed by several runtime factors, including device availability, data quality, and device placement. To this end, we present SensiX, a personal edge platform that stays between sensor data and sensing models, and ensures best-effort inference under any condition while coping with device and data variabilities without demanding model engineering. SensiX externalises model execution away from applications, and comprises of two essential functions, a translation operator for principled mapping of device-to-device data and a quality-aware selection operator to systematically choose the right execution path as a function of model accuracy. We report the design and implementation of SensiX and demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
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