Keynote: "Open, Secure, Near-Sensor Analytics: A Parallel Ultra-Low Power (PULP) Approach"
Luca Benini Architecture LP
Luca Benini holds the chair of digital Circuits and systems at ETHZ and is Full Professor at the
Universita di Bologna. Dr. Benini's research interests are in energy-efficient computing
systems design, from embedded to high-performance. He is also active in the design ultra-low power
VLSI Circuits and smart sensing micro-systems. He has published more than 900 peer-reviewed papers
and five books. He is a Fellow of the IEEE, of the ACM and a member of the Academia Europaea.
He is the recipient of the 2016 IEEE CAS Mac Van Valkenburg award.
Raul Camposano Analog verification
Claudionor Coelho Google / ML support
Keynote: "Solving Scalability Problems in EDA through Optimization"
Laleh Behjat Professor
Laleh Behjat is a Professor in the department of Electrical and Computer Engineering, Schulich School of Engineering, University of
Calgary. She joined the University of Calgary in 2002. Dr. Behjatâ€™s research focus is on developing electronic design automation (EDA)
techniques for physical design and application of large-scale optimization in EDA. Her research team has won several awards including
1st and 2nd places in ISPD 2014 and ISPD 2015 High Performance Routability Driven Placement Contests and 3rd place in DAC Design Perspective
Challenge in 2015. She is an Associate Editor of the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems and Optimization in Engineering from Springer. Dr. Behjat has been developing new and innovative methods to teach EDA to students. She
acted as an academic advisor for of Google Technical Development Guide and has won several awards for her efforts in education including 2017
Killam Graduate Student Supervision and Mentorship Award. Her team, Schulich Engineering Outreach Team, was also the recipient of the ASTech
Leadership Excellence in Science and Technology Public Awareness Award in 2017.
The Integrated Circuits industry has seen an explosion in the numbers of the transistors that are being used while at the same time the
sizes of these transistors have been shrinking. These opposite forces have on one hand forced the engineers to solve very large-scale problems
consisting of billions of transistors, while on the other hand, they had to deal with the uncertainties arising from the very small scales of the transistors. In this talk, we will discuss how optimization and machine learning can be used to solve the problems of extremely large or
extremely small scales. In particular, we will focus on the convex optimization techniques and concepts that be used or adapted to solve
the problems seen in the physical design of integrated circuits.