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Brian Hoskins (Fed)

Brian Hoskins is a research physicist in the Alternative Computing Group in the Nanoscale Device Characterization Division of the Physical Measurement Laboratory (PML). He received both a B.S. and an M.S. in Materials Science and Engineering from Carnegie Mellon University and a Ph.D. in Materials from the University of California, Santa Barbara. For his doctoral research, he developed and characterized resistive switching devices for use in neuromorphic networks. Brian is working on CMOS integration of resistive switches for the development and characterization of intermediate scale neuromorphic networks.

Projects

Selected Publications

  • Optimized stateful material implication logic for three-dimensional data manipulation, G. C. Adam, B. D. Hoskins, M. Prezioso, D.B. Strukov, Nano Research 9, 3914 (2016). [doi]
  • Training and operation of an integrated neuromorphic network based on metal-oxide memristors, M. Prezioso*, F. Merrikh-Bayat*, B.D. Hoskins *, G.C. Adam, K.K. Likharev, and D.B. Strukov, Nature 521, 7550 (2015). [doi] *Equal Contributor
  • Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions, E. Mikheev, B.D Hoskins, D. B. Strukov, and S. Stemmer, Nature Communications 5, 3990 (2014). [doi]

Publications

Room-temperature skyrmions in strain-engineered FeGe thin films

Author(s)
Sujan Budhathoki, Arjun Sapkota, Ka M. Law, Smriti Ranjit, Bhuwan Nepal, Brian Hoskins, Arashdeep S. Thind, Albina Y. Borisevich, Michelle E. Jamer, Travis J. Anderson, Andrew D. Koehler, Karl D. Hobart, Gregory M. Stephen, Don Heiman, Tim Mewes, Rohan Mishra, James C. Gallagher, Adam J. Hauser
Skyrmion electronics hold great promise for low energy consumption and stable high information density, and stabilization of Skyrmion lattice (SkX) phase at or

Streaming Batch Gradient Tracking for Neural Network Training

Author(s)
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
Created May 7, 2019, Updated June 15, 2021