Some recent advancements in machine learning for embedded systems
Online event on Zoom, 10:30 am
- Claudio Turchetti, Università Politecnica delle Marche
Machine learning (ML) techniques are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. In many practical applications the dimensionality of input vectors is very high, so many machine learning techniques incur the “curse of dimensionality” issue, or require depth neural networks (DNN/CNN) with a large number of weights that consume considerable storage and memory bandwidth. Dimensionality reduction of data is a powerful technique that aims to transform high-dimensional data into a desired low-dimensional representation, thus reducing the computational cost of ML applications. The aim of this talk is twofold, first we will present some recent approaches for dimensionality reduction of data with particular emphasis to Manifold Learning , secondly techniques for compression of Convolution Neural Network (CNN), to allow their usage in resource-limited embedded devices, will be discussed.
Keywords: Machine Learning, Nonlinear Dimensionality Reduction, Intrinsic Dimension, Manifold Learning, CNN Compression
Event dedicated to: PhD students, researchers in Electrical and Electronics Engineering, Computer Science, Information Sciences. Participants are invited to ask questions via chat.
About the speaker
Claudio Turchetti received the Laurea degree in electronics engineering from the University of Ancona, Ancona, Italy, in 1979. He joined the Università Politecnica delle Marche, Ancona, in 1980, where was the Head of the Department of Electronics, Artificial Intelligence and Telecommunications for five years and is currently a Full Professor of Micro-Nanoelectronics and Embedded Systems.
His current research interests include: statistical device modeling, RF integrated circuits, device modeling at nanoscale, computational intelligence, signal processing, pattern recognition, system identification, machine learning and neural networks.
He has published more than 160 journal and conference papers, and two books.
The most relevant papers were published in IEEE J. of Solid-State Circuits, IEEE Trans. on Electron Devices, IEEE Trans. on CAD of IC’s and Systems, IEEE Trans. on Neural Networks and Learning Systems, IEEE Trans. on Signal Processing, IEEE Trans. on Cybernetics, IEEE J. of Biomedical and Health Informatics, IEEE Trans. on Consumer Electronics, Information Sciences.
He has held a variety of positions as Project Leader in several applied research programs developed in cooperation with small, large, and multinational companies in the field of microelectronics. At present is involved in three European Projects. Prof. Turchetti has served as a Program Committee Member for several conferences and as a reviewer of several scientific journals. He is a Member of the IEEE, Computational Intelligence and Signal processing Society. He has been an Expert Consultant of the Ministero dell’Università e Ricerca.
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