IEEE 5th World Forum on Internet of Things
15-18 April 2019 – Limerick, Ireland

Track Speakers

Michele Magno

Michele MagnoMichele Magno is currently a Senior Researcher and lecturer at ETH Zürich, Switzerland at the Department of Information Technology and Electrical Engineering (D-ITET) . He received his master and Ph.D. degrees in electronic engineering from the University of Bologna, Italy, in 2004 and 2010, respectively. He was postdoctoral research at Tyndall Institute, Ireland and University College Cork, Ireland, and a visiting professor at University of Nice and ENSSAT University of Rennes, France. His current research interests include smart sensing, low power machine learning, wireless sensor networks, wearable devices, energy harvesting, low power management techniques, and extension of the lifetime of batteries-operating devices. He has authored more than 140 papers in international journals and conferences. Some of  his publications were awarded as best papers in IEEE conferences such as IEEE International Conference on E-health Networking, Application & Services 2018, IEEE Sensors Applications Symposium (SAS) 2018, IEEE International Workshop on Advances in Sensors and Interfaces 2017 among others.  He is a senior IEEE member and an ACM member.

Talk Title: Smart Sensing in the IoT Era: Machine Learning on Ultra Low Power Microcontrollers

Machine Learning (ML) and artificial intelligence are pervading the digital society. Today, even low power embedded systems are incorporating ML, becoming increasingly “smart.” This talk gives an overview of ML methods and algorithms to process and extract useful near-sensor information in end-nodes of the “internet-of-things”, using low-power microcontrollers ARMCortex-M; Bluetooth low energy SoC, and other low power microcontrollers. The main objective is to show how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers. Finally, the talk will introduce an open source library based on Fast Artificial neural network, that allows easily to implement Artificial Neural Network on ARM Cortex-M Family.