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Matthew Daniels (Fed)

Matthew Daniels is a staff physicist in the Alternative Computing Group in the Nanoscale Device Characterization Division of the Physical Measurement Laboratory (PML). He received a B.S. in physics from Clemson University and a Ph.D. in physics from Carnegie Mellon University. For his doctoral research, he developed a semiclassical formalism for exposing a novel, spin-like degree of freedom in antiferromagnetic spin waves. Matthew is working on theoretical models for neuromorphic computing with spintronic devices and on using physics to understand, develop, and quantify energy-efficient computing schemes, information encodings, and coprocessors.

Selected Publications

  • Spin-transfer torque induced spin waves in antiferromagnetic insulators, M. W. Daniels, W. Guo, G. M. Stocks, D. Xiao, and J. Xiao, New Journal of Physics 17, 103039 (2015).
  • Antiferromagnetic Spin Wave Field-Effect Transistor, R. Cheng, M. W. Daniels, J.-G. Zhu, and D. Xiao, Scientific Reports 6, 24223 (2016).
  • Ultrafast switching of antiferromagnets via spin-transfer torque, R. Cheng, M. W. Daniels, J.-G. Zhu, and D. Xiao, Physical Review B 91, 064423 (2015).

Publications

Neural networks three ways: unlocking novel computing schemes using magnetic tunnel junction stochasticity

Author(s)
Matthew Daniels, William Borders, Nitin Prasad, Advait Madhavan, Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Lei Wan, Michael Tran, Jordan Katine, Daniel Lathrop, Brian Hoskins, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland
Due to their interesting physical properties, myriad operational regimes, small size, and industrial fabrication maturity, magnetic tunnel junctions are

Device Modeling Bias in ReRAM-Based Neural Network Simulations

Author(s)
Imtiaz Hossen, Matthew Daniels, Martin Lueker-Boden, Andrew Dienstfrey, Gina Adam, Osama Yousuf
The study of resistive-RAM (ReRAM) devices for energy efficient machine learning accelerators requires fast and robust simulation frameworks that incorporate

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions

Author(s)
Jonathan Goodwill, Nitin Prasad, Brian Hoskins, Matthew Daniels, Advait Madhavan, Lei Wan, Tiffany Santos, Michael Tran, Jordan Katine, Patrick Braganca, Mark Stiles, Jabez J. McClelland
The increasing scale of neural networks and their growing application space have produced a demand for more energy and memory efficient artificial-intelligence

Patents (2018-Present)

Quasi-Systolic Processor and Quasi-Systolic Array

NIST Inventors
Brian Hoskins , Matthew Daniels , Mark D. Stiles and Advait Madhavan
A quasi-systolic array includes: a primary quasi-systolic processor; an edge row bank and edge column bank of edge quasi-systolic processors; and an interior bank of interior quasi-systolic processors. The primary quasi-systolic processor, edge quasi-systolic processor, and interior quasi-systolic

Timing-Based Computer Architecture Systems And Methods

NIST Inventors
Advait Madhavan , Mark D. Stiles and Matthew Daniels
Tropical algebra is an emerging field of mathematics concerned with graph theory, control theory, and certain optimization problems, especially in discrete event systems. We developed a novel computer circuit called a tropical state machine that uses signal timing and the physics of nanodevices to
Created July 30, 2019, Updated December 9, 2022