U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: Matthew Daniels (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 26 - 35 of 35

Temporal Memory with Magnetic Racetracks

December 1, 2020
Author(s)
Hamed Vakili, Mohammed N. Sakib, Samiran Ganguly, Mircea Stan, Matthew Daniels, Advait Madhavan, Mark D. Stiles, Avik W. Ghosh
Race logic is a relative timing code that represents information in a wavefront of digital edges on a set of wires in order to accelerate dynamic programming and machine learning algorithms. Skyrmions, bubbles, and domain walls are mobile magnetic

Streaming Batch Gradient Tracking for Neural Network Training

April 3, 2020
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 multiplication and repeatedly moving neural network models in and out of memory motivates a search

Energy-efficient stochastic computing with superparamagnetic tunnel junctions

March 5, 2020
Author(s)
Matthew W. Daniels, Advait Madhavan, Philippe Talatchian, Alice Mizrahi, Mark D. Stiles
Stochastic computing has been limited by the inaccuracies introduced by correlations between the pseudorandom bitstreams used in the calculation. We hybridize a stochastic version of magnetic tunnel junctions with basic CMOS logic gates to create a

Streaming Batch Eigenupdates for Hardware Neural Networks

August 6, 2019
Author(s)
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 for their increased energy efficiency and density in comparison to graphics processing units