Machine Learning on VLSI

About the track


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.


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


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.


Dr. Ing. Pedro Julián (UNS, CONICET)

Dr. Pedro Julián (UNS, Bahía Blanca) holds a PhD in Systems Control from Universidad Nacional del Sur, Bahía Blanca, 1999. He is currently Principal Investigator of the National Council for Scientific and Technical Research (CONICET), Visiting Associate Professor of the Department of Electrical and Computer Engineering at Whiting School of Engineering, The Johns Hopkins University, Associate Professor with exclusive dedication at the National University of the South and Director of Micro and Nano Electronics Laboratory at the Universidad Nacional del Sur.

Ing. Nicolás Daniel Rodríguez (Ph.D. Candidate)

Nicolás was born in 1994 in Bahía Blanca, Argentina.
He received his Electronic Engineering degree from Universidad Nacional del Sur (UNS), in March 2018.
Since April 2018, he started his Ph.D. in Engineering at UNS, and his research interests are low power microelectronics, deep learning algorithms design, image processing, embedded systems applications, and neural network accelerators.

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.


Preparation content