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Tiny Machine Learning: deeper and wider machine learning at the edge

Registraion URL: https://register.gotowebinar.com/register/6231880598200319500

Wed, Sep 14, 2022 8:00 AM - 9:00 AM EDT

Abstract: The “computing everywhere” paradigm (comprising Internet-of-Things and Edge Computing) will pave the way for a pervasive diffusion of Tiny Machine Learning (TinyML) in everyday life. To fully address this challenge TinyML solutions must become deeper, hence encompassing the deep-learning paradigms being the state-of-the-art in many recognition and classification applications, and wider, hence being able to operate in a collaborative and federated way within an ecosystem of heterogenous technological objects. This talk explores the solutions and methodologies to make TinyML deeper and wider by also considering the role of an effective and efficient processing of encrypted-data through deep-learning-as-a-service in an heterogeneous-hardware ecosystem. Speaker Bio: Manuel Roveri received the Ph.D. degree in Computer Engineering from the Politecnico di Milano (Italy) and the MS in Computer Science from the University of Illinois at Chicago (USA). He has been Visiting Researcher at Imperial College London (UK). Currently, he is an Associate Professor at the Department of Electronics and Information of the Politecnico di Milano (Italy). Current research activity addresses Embedded and Edge AI, Learning in presence of Concept Drift and Intelligent Embedded and Cyber-physical Systems. Manuel Roveri is a Senior Member of IEEE and served as Chair and Member in several IEEE Committees. He holds 1 patent and has published about 100 papers in international journals and conference proceedings He is the recipient of the 2018 IEEE Computational Intelligence Magazine “Outstanding Paper Award” and of the 2016 IEEE Computational Intelligence Society “Outstanding Transactions on Neural Networks and Learning Systems Paper Award”.

 

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