Mar 30, 2022
In this episode of Intel on AI host Amir Khosrowshahi, assisted by Dmitri Nikonov, talks with Jean Anne Incorvia about the use of new physics in nanocomputing, specifically with spintronic logic and 2D materials.
Jean is an Assistant Professor and holds the Fellow of Advanced Micro Devices Chair in Computer Engineering in the Department of Electrical and Computer Engineering at The University of Texas at Austin, where she directs the Integrated Nano Computing Lab.
Dimitri is a Principal Engineer in the Components Research at Intel. He holds a Master of Science in Aeromechanical Engineering from the Moscow Institute of Physics and Technology and a Ph.D. from Texas A&M. Dimitri works in the discovery and simulation of nanoscale logic devices and manages joint research projects with multiple universities. He has authored dozens of research papers in the areas of quantum nanoelectronics, spintronics, and non-Boolean architectures.
In the episode Jean talks about her background with condensed matter physics and solid-state electronics. She explains how magnetic properties and atomically thin materials, like graphene, can be leveraged at nanoscale for beyond-CMOS computing. Jean goes into detail about domain wall magnetic tunnel junctions and why such devices might have a lower energy cost than the modern process of encoding information in charge. She sees these new types of devices to be compatible with CMOS computing and part of a larger journey toward beyond-von Neumann architecture that will advance the evolution of artificial intelligence, neural networks, deep learning, machine learning, and neuromorphic computing.
The episode closes with Jean, Amir, and Dimitri talking about the broadening definition of quantum computing, existential philosophy, and AI ethics.
Academic research discussed in the podcast episode:
- Being and Time
- Cosmic microwave background radiation anisotropies: Their discovery and utilization
- Nanotube Molecular Wires as Chemical Sensors
- Visualization of exciton transport in ordered and disordered molecular solids
- Nanoscale Magnetic Materials for Energy-Efficient Spin Based Transistors
- Lateral Inhibition Pyramidal Neural Network for Image Classification
- Magnetic domain wall neuron with lateral inhibition
- Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing
- Domain wall-magnetic tunnel junction spin–orbit torque devices and circuits for in-memory computing
- High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network