Machine Learning on VLSI

About the track

Description

It is the newest course offered by the EAMTA. In this track, the students acquire basic notions of Machine Learning techniques and their implementation on integrated circuits.

Track goal

The goal of this course is to give a comprehensive overview of deep neural networks, including training techniques and network synthesis. Additionally, the course will cover the implementation of neural network components and the consideration for data input and output.

A final project will be proposed that includes the design of a neural network for a specific problem and the creation of an architecture for its implementation.

Prerequisites

This course is intended for advanced students, graduate students, and professionals who have a background in VLSI. If you do not have this background, it is recommended that you take the basic VLSI track.

Minimum content

Introduction: neural networks on chip
Basic concepts of Machine Learning (ML): network and layers
Introduction to Pytorch
Case study
VLSI implementation of Machine Learning blocks
Advanced structures of ML

Modality

The instructors will teach the course remotely, and for the audience, the mode can be virtual or in-person (this only applies to this track). In either case, the registration fee must still be paid.

Professors

Dr. Eng. Pedro Julián (UNS, CONICET)

Dr. Pedro Julián (UNS, Bahía Blanca) holds an Electronics Engineer and PhD degrees in Control Systems both from Universidad Nacional del Sur (UNS), Bahía Blanca. He is currently Site Manager of the Bahia Blanca Allegro Design Center and Associate Professor at UNS.

Dr. Eng. Luciana De Micco

Dr. Luciana De Micco (UNMdP, Mar del Plata) holds a PhD in Engineering from the National University of Mar del Plata (UNMdP), Argentina. She is currently an Independent Researcher of the National Council for Scientific and Technical Research (CONICET), Full Professor at the Faculty of Engineering, National University of Mar del Plata, and a member of the Institute for Scientific and Technological Research in Electronics (ICYTE), UNMdP–CONICET.

Ing. Diego Gigena Ivanovich (Ph.D. Candidate)

Diego was born in 1994 in Trelew, Argentina. He received his Electronic Engineering degree from Universidad Nacional de la Patagonia San Juan Bosco (UNPSJB) in March 2018. In April 2018, he began his Ph.D. in Engineering at Universidad Nacional del Sur (UNS). He is currently a Junior Scientist in the eAI Team at Silicon Austria Labs

Menzatiuk Oleksandra, BSc

Oleksandra got her Bachelor’s degree in Computer Science at Taras Shevchenko National University of Kyiv, Ukraine. She is currently pursuing her Master’s degree in Artificial Intelligence at Johannes Kepler University Linz, Austria while working as part of Embedded Artificial Intelligence team in Silicon Austria Labs.