By Ken Duffy (Northeastern University)

Talk Abstract:This talk is the first part of a two-talk series by Médard and Duffy. At the last Cognition & Control workshop, we overviewed GRAND, guessing random additive noise decoding, a universal decoder that has optimal or near-optimal performance. In this part, we consider the hardware realization of GRAND with soft information, according to the ordered reliability bits (ORB) GRAND approach. He highlight how careful co-design of algorithms and hardware implementations can lead to record-breaking performance, in this case by breaking the 1 pJ per decoded bit barrier.

Speaker Bio: Ken R. Duffy is a professor at Northeastern University with a joint appointment in the Department of Electrical & Computer Engineering, where he is interim chair, and the Department of Mathematics. He works in works collaborative multi-disciplinary teams to design, analyze, and realize algorithms using tools from probability, statistics, and machine learning. Algorithms he has developed have been implemented in digital circuits and in DNA. He received a B.A (mod) in Mathematics in 1996 and a PhD in Applied Probability in 2000, both awarded by Trinity College Dublin. He was previously a professor at National University of Ireland, Maynooth, where he was the Director of the Hamilton Institute, an interdisciplinary research centre, from 2016 to 2022. He was one of three co-Directors of the Science Foundation Ireland Centre for Research Training in Foundations of Data Science, which has funded more than 120 PhD students. He is a co-founder of the Royal Statistical Society’s Applied Probability Section (2011), co-authored a cover article of Trends in Cell Biology (2012), is a winner of a best paper award at the IEEE International Conference on Communications (2015), the best paper award from IEEE Transactions on Network Science and Engineering (2019), the best research demo award from COMSNETS (2022), the best demo award from COMSNETS (2023). He is an associate editor of IEEE Transactions on Information Theory and of IEEE Transactions on Molecular, Biological, and Multi-scale Communications.