主讲人简介：

Michele Magno received the masters’ and Ph.D. degrees in electronic engineering from the University of Bologna, Bologna, Italy, in 2004 and 2010, respectively. He is currently a Senior Researcher and lecturer with ETH Zürich, Zürich, Switzerland. He is senior member IEEE since 2016. and He has authored more than 150 papers in international journals and conferences, few of them awarded as best paper. His current research interests include wireless sensor networks, machine learning on low power micro-controllers, smart wearable devices, energy harvesting, low power management techniques, and extension of the lifetime of batteries-operating devices.

Abstract：

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, e.g. ARM Cortex-M, Bluetooth low energy SoC, and others. The main objective is to show how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers with both the popular ARM-Cortex-M family and novel Parallel Ultra Low Power (PULP) family based on RISC-V cores. Finally, the talk will introduce an open-source toolkit based on Fast Artificial Neural Network library, that allows fast deployment of Artificial Neural Network on ARM Cortex-M Family and PULP. The talk will also show with use-case examples, the benefits of energy-efficient ML to achieve self-sustaining smart sensing.

报名截止时间：2020年01月06日17:00

报名链接：https://www.wjx.cn/jq/52527949.aspx

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