tinyML Talks: Machine Learning without batteries: the case for light-powered tinyML

Over the last decade, energy harvesting has seen significant growth as different markets adopt green, sustainable ways to produce electrical energy. Even though costs have fallen, the embedded machine learning and Internet of Things community have not yet widely adopted energy-harvesting-based solutions. In this talk, I will present a design methodology for smart batteryless sensors, capable of gathering data, processing it, and transmitting inference results wirelessly. A newly developed gesture detection feature for the open-source MiroCard will be presented, along with the cost-benefits and privacy implications of batteryless sensing systems.

Date

March 3, 2022

Location

Virtual

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Schedule

Timezone: PST

Machine Learning without batteries: the case for light-powered tinyML

Andres GOMEZ, Postdoctoral Fellow

University of St. Gallen

Andres GOMEZ, Postdoctoral Fellow

University of St. Gallen

Andres Gomez received a dual degree in electronics engineering and computer engineering from the Universidad de Los Andes, Colombia, an M.Sc. degree from the ALaRI Institute (Università della Svizzera Italiana), Switzerland, and a Ph.D. from ETH Zurich, Switzerland. He has over ten years of experience with embedded systems and has worked in multiple research laboratories in Colombia, Italy, and Switzerland. More recently, he has worked as an R&D engineer at Miromico AG. He has co-authored over 30 scientific articles and has contributed to multiple open-source projects. He is currently a Postdoctoral Fellow at the University of St. Gallen, Switzerland. His current research interests include batteryless system design, the Internet of Things, and the Web of Things.

Schedule subject to change without notice.