We introduce the datasheet for ML sensors framework. We provide a comprehensive template, collaboratively developed in academia-industry partnerships, that captures the distinct attributes of ML sensors, including hardware specifications, ML model and dataset characteristics, end-to-end performance metrics, and environmental impacts. Our framework addresses the continuous streaming nature of sensor data, real-time processing requirements, and embeds benchmarking methodologies that reflect real-world deployment conditions, ensuring practical viability.
In this viewpoint we propose the ML sensor: a logical framework for developing ML-enabled embedded systems which empowers end users through its privacy-by-design approach. By limiting the data interface, the ML sensor paradigm helps ensure that no user information can be extracted beyond the scope of the sensor’s functionality. Our proposed definition is as follows: An ML sensor is a self-contained, embedded system that utilizes machine learning to process sensor data on-device – logically decoupling data computation from the main application processor and limiting the data access of the wider system to high-level ML model outputs.