tinyML Summit 2022: Sensing Applications as a Driver for TinyML Solutions
tinyML Summit 2022
Sensing Session
Sensing Applications as a Driver for TinyML Solutions
Victor PANKRATIUS, Director / Head of Global Software Engineering, Bosch Sensortec GmbH
New generations of sensors are increasingly equipped with microcontrollers and computing capabilities that enable local machine learning in millimeter-sized packages. This talk presents examples and use cases where sensing applications have become a major driver for TinyML in ultra-low power contexts. Applications are shown for intelligent Micro-Electro-Mechanical Systems (MEMS) in motion learning, sports analytics, and gas and environmental sensing. Looking at the software stack, the talk also addresses the importance of formalizing and including domain knowledge into ML as an additional leverage for optimizations, such as shrinking memory footprints, making trade-offs in signal processing, and algorithmic choice. Learning from individual success stories, our insights help sketch a bigger picture for TinyML ecosystems and platforms that are beginning to take shape, and how various groups and communities can be engaged.
Видео tinyML Summit 2022: Sensing Applications as a Driver for TinyML Solutions канала The tinyML Foundation
Sensing Session
Sensing Applications as a Driver for TinyML Solutions
Victor PANKRATIUS, Director / Head of Global Software Engineering, Bosch Sensortec GmbH
New generations of sensors are increasingly equipped with microcontrollers and computing capabilities that enable local machine learning in millimeter-sized packages. This talk presents examples and use cases where sensing applications have become a major driver for TinyML in ultra-low power contexts. Applications are shown for intelligent Micro-Electro-Mechanical Systems (MEMS) in motion learning, sports analytics, and gas and environmental sensing. Looking at the software stack, the talk also addresses the importance of formalizing and including domain knowledge into ML as an additional leverage for optimizations, such as shrinking memory footprints, making trade-offs in signal processing, and algorithmic choice. Learning from individual success stories, our insights help sketch a bigger picture for TinyML ecosystems and platforms that are beginning to take shape, and how various groups and communities can be engaged.
Видео tinyML Summit 2022: Sensing Applications as a Driver for TinyML Solutions канала The tinyML Foundation
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