tinyML Summit 2023: Machine Learning Sensor Certifications and tinyML edu Update
Machine Learning Sensor Certifications
Vijay JANAPA REDDI, Associate Professor, Harvard University
There are billions of microcontrollers worldwide, and we are on the verge of a new data-centric paradigm: putting machine learning intelligence into embedded microcontrollers. This paradigm is made possible by improvements in TinyML methods, tools, and technologies. This new idea of “machine learning sensors” (ML Sensors) is a significant change for the embedded ecosystem. ML sensors raise new concerns about the privacy and security of sensitive user data and the portability and ease of integrating them into the existing ecosystem. Because of this, we need a framework for the practical, responsible, and efficient deployment of ML sensors as a community. To this end, the talk will focus on defining ML sensors and the challenges and opportunities of bringing a new generation of embedded sensor technology to the market.
This talk is not about a point solution; instead, it discusses the framework that needs to be
implemented for ML sensors to be realized in practice. Achieving this requires knowing ML
sensors’ technical and ethical implications before they are developed and distributed.
Nevertheless, one of the most critical issues to address is the concept of a datasheet for ML
sensors. Future sensors must be clear and transparent about what they do and how they do it.
Such information can be enshrined within a datasheet analogous to a traditional sensor
datasheet. Hence, we will focus on the following three topics:
1. Interface – What universal interface is needed for ML Sensors?
2. Standards – What standards need to be in place for ML Sensors?
3. Ethics – What ethical considerations are needed for ML Sensors?
• Call to Action for the TinyML Community
We need the community’s help to develop and deploy ML sensors safely and reliably so they
can reach their full potential. Therefore, the first goal of this talk is to educate the community about the implications of developing ML sensors and understand how we can develop them systematically so that they are both efficient and effective in the existing embedded ecosystem
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