Joseph Friedman

Joseph S. Friedman is an Associate Professor of Electrical & Computer Engineering at the University of Texas at Dallas, leading the NeuroSpinCompute Laboratory within the Erik Jonsson School of Engineering and Computer Science. His research focuses on unconventional computing paradigms leveraging nanotechnology, including neuromorphic systems, spintronics, and memristive devices. He specializes in nanomagnet-based logic architectures, neuromorphic computing with domain walls and skyrmions, and hardware security for emerging technologies.

His research explores energy-efficient computing through novel paradigms such as reversible skyrmion logic, neuromorphic networks using magnetic tunnel junctions, and stochastic Bayesian inference circuits. He has pioneered spintronic neurons demonstrating 94% accuracy in handwritten digit recognition and developed secure logic locking mechanisms using nanomagnet logic. His work integrates experimental fabrication with SPICE modeling, emphasizing scalable beyond-CMOS systems.

Recent advancements include toggle SOT-MRAM architectures, quantum circuit design for neutral atom systems, and neuromorphic networks leveraging superconducting flux quanta. He advises over 20 graduate and undergraduate students, fostering innovation in AI hardware and unconventional computing.

Notable projects include the NeuroSpinCompute Lab's domain wall neuromorphic networks, secure logic locking schemes, and collaborations with institutions like Sandia National Labs on neuromorphic reservoir computing. Current research trends emphasize low-energy spintronic architectures, hybrid quantum-classical systems, and neuromorphic applications in edge computing.

His research is supported by NSF grants CCF-1910800 and CCF-2146439, focusing on neuromorphic and spintronic systems. He regularly contributes to conferences like IEEE Rebooting Computing and SPIE Spintronics, showcasing breakthroughs in nanomagnetic logic and neuromorphic inference.