Machine Learning @ the Extreme Edge

Today's challenge is real-time and energy-efficient information extraction and processing at the edge using Artificial Intelligence. However, there is a recent trend to implement machine learning on devices located on the extreme edge, i.e. the border between the analog, physical world and digital world. These devices consist of one or more sensors and a resource-constrained embedded device, i.e. a device with limited memory, computing power, and power consumption. Today's challenge is the development of accurate, energy-efficient machine learning models for deployment on these resource-constrained devices.

"This image was created with the assistance of DALL·E 2."

Machine Learning at the Extreme Edge (ML@E2dge) looks at how we can apply machine learning in the development of accurate, energy-efficient, and intelligent (wireless) (battery-powered) devices. Starting from a case study, a model is developed using embedded machine learning.

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