Dr. Joseph S. Friedman

Joe Friedman

University of Texas at Dallas

Dr. Joseph S. Friedman is an associate professor of Electrical & Computer Engineering at The University of Texas at Dallas and director of the NeuroSpinCompute Laboratory. He holds a Ph.D. and M.S. in Electrical & Computer Engineering from Northwestern University and undergraduate degrees from Dartmouth College. He was previously a CNRS Research Associate with Université Paris-Saclay, a Summer Faculty Fellow at the U.S. Air Force Research Laboratory, a Visiting Professor at Politecnico di Torino, a Guest Scientist at RWTH Aachen University, and worked on logic design automation at Intel Corporation.Dr. Friedman is a member of the editorial boards of Scientific Reports and IEEE Transactions on Nanotechnology, and previously the Microelectronics Journal. He is a conference chair of SPIE Spintronics, has served on numerous conference technical program committees, and is the founder and chairperson of the Texas Symposium on Computing with Emerging Technologies (ComET). He has also been awarded the National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) Award.

Reversible, Neuromorphic, Reservoir, and Secure Computing with Spintronic Phenomena

The rich physics present in a wide range of spintronic materials and devices provide opportunities for a variety of computing applications. This presentation will describe six distinct proposals to leverage spintronic phenomena for reversible computing, neuromorphic computing, reservoir computing, and hardware security. The presentation will begin with a solution for reversible computing in which magnetic skyrmions propagate and interact in a scalable system with the potential for energy dissipation below the Landauer limit, followed by a paradigm for operating Boolean logic at terahertz clock frequencies utilizing the magnetoresistance of low-dimensional materials. Three neuromorphic systems for emulating neurobiological behavior with spintronic phenomena will then be presented: a purely-spintronic system that enables unsupervised learning with magnetic domain wall neurons and synapses, a reservoir computing system based on the dynamics of frustrated nanomagnets, and an approach for unsupervised learning that marks the first experimental demonstration of a neuromorphic network directly implemented with MTJ synapses. This presentation will conclude with a logic locking paradigm based on nanomagnet logic, the first logic locking system that is secure against both physical and algorithmic attacks.