NIST only participates in the February and August reviews.
| name |
email |
phone |
|
| Jabez J. McClelland |
jabez.mcclelland@nist.gov |
301.975.3721 |
| Mark David Stiles |
mark.stiles@nist.gov |
301.975.3745 |
The Alternative Computing Group at NIST has an ongoing program developing new metrologies to support emerging information processing technologies such as neuromorphic computing and artificial intelligence. Work involves studying individual devices, building prototype circuits, and theoretically analyzing new architectures utilizing exotic devices such as magnetic tunnel junctions, metal-oxide and phase-change memristors, and others. These devices are combined with custom-designed conventional CMOS circuits to realize diverse functionalities, from crossbar-based machine learning to race-logic-based computing. Opportunities exist for experimental work in device fabrication and measurement using NIST’s state-of-the-art NanoFab and extensive measurement capabilities, CMOS circuit design work for foundry tape-out, and theoretical work developing new algorithms and architectures that leverage the low-energy, high-speed properties of emerging devices. The ultimate goal is to explore the landscape of emerging hardware-based artificial intelligence systems to better understand the role played by measurement and metrology in these complex, adaptive systems.
S. Gibeault et al., Programmable electrical coupling between stochastic magnetic tunnel junctions, Phys. Rev. Appl. 21, 034064 (2024).
W. A. Borders, A. Madhavan, M. W. Daniels, V. Georgiou, M. Lueker-Boden, T. S. Santos, P. M. Braganca, M. D. Stiles, J. J. McClelland, and B. D. Hoskins, Measurement-driven neural-network training for integrated magnetic tunnel junction arrays, Phys. Rev. Appl. 21, 054028 (2024).
M. W. Daniels et al., Neural Networks Three Ways: Unlocking Novel Computing Schemes Using Magnetic Tunnel Junction Stochasticity, in Spintronics XVI, Vol. 12656 (SPIE, 2023), pp. 84–94.
Neuromorphic computing; artificial intelligence; memristors; RRAM; CMOS; machine learning; cognitive computing; phase change memory; superconducting circuits; magnetic tunnel junctions; race logic