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Matthew Daniels

Matthew Daniels is a NRC Postdoctoral Fellow 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. The work enables researchers to use antiferromagnetic insulators to perform semiclassical quantum computations. Matthew is working with Mark Stiles on theoretical models for neuromorphic computing with spintronic devices, specifically on networks of spin-torque oscillators.

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).


Streaming Batch Gradient Tracking for Neural Network Training

Siyuan Huang, Brian D. Hoskins, Matthew W. Daniels, Mark D. Stiles, Gina C. Adam
Faster and more energy efficient hardware accelerators are critical for machine learning on very large datasets. The energy cost of performing vector-matrix

Streaming Batch Eigenupdates for Hardware Neural Networks

Brian D. Hoskins, Matthew W. Daniels, Siyuan Huang, Advait Madhavan, Gina C. Adam, Nikolai B. Zhitenev, Jabez J. McClelland, Mark D. Stiles
Neuromorphic networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest
Created July 30, 2019, Updated February 21, 2020