We held our next tinyML Talks webcast. Matthew Stewart from Harvard University presented Datasheets for Machine Learning Sensors on July 11, 2023.
Machine learning (ML) sensors have revolutionized the field of sensing, enabling intelligence at the edge and granting users greater control over their data. To support the development of intelligent devices, it is crucial to document ML sensor specifications, functionalities, and limitations comprehensively. This work introduces a standardized datasheet template for ML sensors, covering essential components such as hardware, ML model, dataset attributes, end-to-end performance metrics, and environmental impact. By presenting an exemplar datasheet for our ML sensor, we delve into each section, highlighting its significance. Our objective is to demonstrate how these datasheets enhance understanding and utilization of sensor data in ML applications, offering objective measures to evaluate and compare system performance. ML sensors, accompanied by datasheets, provide improved privacy, security, transparency, explainability, auditability, and user-friendliness for ML-enabled embedded systems. We emphasize the importance of widespread datasheet standardization across the ML community to ensure responsible and effective utilization of sensor data.
Matthew Stewart is a postdoctoral researcher in the Edge Computing Lab at Harvard University. He holds a Ph.D. and MSc in Engineering Sciences and Data Science from Harvard University, and an integrated BEng/MEng in Mechanical Engineering from Imperial College London and the National University of Singapore. Matthew’s research work is highly interdisciplinary, encompassing embedded machine learning, autonomous vehicles, benchmarking tools for reinforcement learning and robotics, sustainable computing, and machine learning sensors. Matthew is also a part-time blogger for Towards Data Science, a co-creator of the HarvardX tinyML courses, and a research coordinator at MLCommons.
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