Machine Learning Sensors

Pete Warden; Matthew Stewart; Brian Plancher; Colby Banbury; Shvetank Prakash; Emma Chen; Zain Asgar; Sachin Katti; Vijay Janapa Reddi

arXiv · 2206.03266

Machine Learning Sensors

Pete Warden·Matthew Stewart·Brian Plancher·Colby Banbury·Shvetank Prakash·Emma Chen·Zain Asgar·Sachin Katti·Vijay Janapa Reddi

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Abstract

Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from complex integration, lack of modularity, and privacy and security concerns from data movement. This article proposes a more data-centric paradigm for embedding sensor intelligence on edge devices to combat these challenges. Our vision for "sensor 2.0" entails segregating sensor input data and ML processing from the wider system at the hardware level and providing a thin interface that mimics traditional sensors in functionality. This separation leads to a modular and easy-to-use ML sensor device. We discuss challenges presented by the standard approach of building ML processing into the software stack of the controlling microprocessor on an embedded system and how the modularity of ML sensors alleviates these problems. ML sensors increase privacy and accuracy while making it easier for system builders to integrate ML into their products as a simple component. We provide examples of prospective ML sensors and an illustrative datasheet as a demonstration and hope that this will build a dialogue to progress us towards sensor 2.0.

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BibTeX RIS

Pete Warden, Matthew Stewart, Brian Plancher, Colby Banbury, Shvetank Prakash, Emma Chen, Zain Asgar, Sachin Katti, Vijay Janapa Reddi. 2022-06-07. Machine Learning Sensors. https://arxiv.org/abs/2206.03266

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