JUMP e-Workshop: Using ASCENT Technologies in Support of Few-Shot Machine Learning Models and Homomorphic Encryption Algorithms


Location: webex

In this presentation, Prof. Michael Niemier and Dayane Reis (Notre Dame) will discuss how technologies being studied within ASCENT (e.g., ferroelectric field effect transistors (FeFETs), resistive random access memory (RRAM), etc.) can be applied in support of few-shot learning models (with an emphasis on the support of lifelong learning), as well as to support and accelerate homomorphic encryption algorithms.

Bio:  Mike Niemier is currently an Associate Professor at the University of Notre Dame.  His research interests include designing, facilitating, benchmarking, and evaluating circuits and architectures based on emerging technologies. Currently, Niemier's research efforts are based on new transistor technologies, as well as devices based on alternative state variables such as spin.  He is the recipient of an IBM Faculty Award, the Rev. Edmund P. Joyce, C.S.C. Award for Excellence in Undergraduate Teaching at the University of Notre Dame, and best paper awards such as at ISLPED in 2018.  Niemier has served on numerous technical program committees for design related conferences (including DAC, DATE, ICCAD, etc.), and has chaired the emerging technologies track at DATE, DAC, and ICCAD.  He is an associate editor for IEEE Transactions on Nanotechnology, as well as the ACM Journal of Emerging Technologies in Computing.  He is a senior member of the IEEE.


This e-Workshop is only available to the JUMP research community, such as Principal Investigators, postdoc researchers, students, and corporate sponsors. ASCENT is one of six JUMP centers administered by SRC. For access to full program information, please go to src.org. Thank you for interest in ASCENT.