Creating and sharing knowledge for telecommunications

Electron Devices Distinguished Lecture: Signal Integrity and the Emerging Challenges of High-Speed Nanoscale Interconnects

on 12-05-2023

... Do not miss the Electron Devices Distinguished Lecture: Signal Integrity and the Emerging Challenges of High-Speed Nanoscale Interconnects by Prof. Ramachandra Achar.

With the increasing demands for higher signal speeds coupled with the need for decreasing feature sizes, signal integrity effects such as delay, distortion, reflections, crosstalk, ground bounce and electromagnetic interference have become the dominant factors limiting the performance of high-speed systems. These effects can be diverse and can seriously impact the design performance at all hierarchical levels including integrated circuits, printed circuit boards, multi-chip modules and backplanes. If not considered during the design stage, signal integrity effects can cause failed designs. Since extra iterations in the design cycle are costly, accurate prediction of these effects is a necessity in high-speed designs. Consequently, preserving signal integrity has become one of the most challenging tasks facing designers of modern multifunction and miniature electronic circuits and systems. This talk provides a comprehensive approach to understanding the multidisciplinary problem of signal integrity: issues/modeling/analysis in high-speed designs.

Dr. Achar is a professor in the electronics engineering department at Carleton University, Ottawa, Ontario. Prior to joining Carleton University (2000), he served in various capacities in leading research labs, including T. J. Watson Research Center, IBM, New York (1995), Larsen and Toubro Engineers Ltd., Mysore (1992), Central Electronics Engineering Research Institute, Pilani, India (1992) and Indian Institute of Science, Bangalore, India (1990).

The lecture will take place on May 12, 2023, in Room LT2, 4th Floor, Torre Norte, Instituto Superior Técnico.

Free event.

More: More Information..

Deep Learning for Inverse Problems: Theoretical Perspectives, Algorithms, and Applications

on 12-05-2023

... Recent years have witnessed a surge of interest in deep learning methods to tackle inverse problems arising in various domains such as medical imaging, remote sensing, and the arts and humanities.
This talk offers an overview of recent advances in the foundations and applications of deep learning for inverse problems, with a focus on model-based deep learning methods.
Concretely, this talk will introduce theoretical advances in the area of model-based learning, including learning guarantees; it will also introduce algorithmic advances in model-based learning; and, finally, it will showcase a portfolio of emerging signal & image processing challenges that benefit from model-based learning.

The lecture will take place on May 12 (14:00), in Room EA2, at Torre Norte, Instituto Superior Técnico.

Miguel Rodrigues is a Professor of Information Theory and Processing at University College London; he leads the Information, Inference, and Machine Learning Lab at UCL, and he has also been the founder and director of the master's program in Integrated Machine Learning Systems at UCL. He is also currently the UCL Turing University Lead and a Turing Fellow with the Alan Turing Institute — the UK National Institute of Data Science and Artificial Intelligence.
He held various appointments with various institutions worldwide including Cambridge University, Princeton University, Duke University, and the University of Porto, Portugal. He obtained an undergraduate degree in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto, Portugal, and a PhD degree in Electronic and Electrical Engineering from University College London.
Miguel Rodrigues's research lies in the general areas of information theory, information processing, and machine learning. His most relevant contributions have ranged from the information-theoretic analysis and design of communications systems, information-theoretic security, information-theoretic analysis and design of sensing systems, and the information-theoretic foundations of machine learning. He serves or has served as Editor of IEEE BITS, Editor of the IEEE Transactions on Information Theory, and Lead Guest Editor of the Special Issue on “Information-Theoretic Methods in Data Acquisition, Analysis, and Processing” of the IEEE Journal on Selected Topics in Signal Processing. He was the recipient of the IEEE Communications and Information Theory Societies Joint Paper Award 2011 and is also an IEEE Fellow.
More Information..