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Search Publications by: Mark D. Stiles (Fed)

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Displaying 1 - 25 of 383

Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions

April 22, 2025
Author(s)
Temitayo Adeyeye, Sidra Gibeault, Daniel Lathrop, Matthew Daniels, Mark Stiles, Jabez McClelland, William Borders, Jason Ryan, Philippe Talatchian, Ursula Ebels, Advait Madhavan
Though exponential distributions are ubiquitous in statistical physics and related computational models, sampling them from device behavior is rarely done. The superparamagnetic tunnel junction (SMTJ), a key device in probabilistic computing, shows

Measurement-driven Langevin modeling of superparamagnetic tunnel junctions

July 23, 2024
Author(s)
Liam Pocher, Temitayo Adeyeye, Sidra Gibeault, Philippe Talatchian, Ursula Ebels, Daniel Lathrop, Jabez J. McClelland, Mark Stiles, Advait Madhavan, Matthew Daniels
Superparamagnetic tunnel junctions are important devices for a range of emerging technologies, but most existing compact models capture only their mean switching rates. Capturing qualitatively accurate analog dynamics of these devices will be important as

Measurement-driven neural-network training for integrated magnetic tunnel junction arrays

May 14, 2024
Author(s)
William Borders, Advait Madhavan, Matthew Daniels, Vasileia Georgiou, Martin Lueker-Boden, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland, Brian Hoskins
The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann

Programmable electrical coupling between stochastic magnetic tunnel junctions

March 29, 2024
Author(s)
Sidra Gibeault, Temitayo Adeyeye, Liam Pocher, Daniel Lathrop, Matthew Daniels, Mark Stiles, Jabez J. McClelland, William Borders, Jason Ryan, Philippe Talatchian, Ursula Ebels, Advait Madhavan
Superparamagnetic tunnel junctions (SMTJs) are promising sources of randomness for compact and energy efficient implementations of various probabilistic computing techniques. When augmented with electronic circuits, the random telegraph fluctuations of the

Unbiased random bitstream generation using injection-locked spin-torque nano-oscillators

March 29, 2024
Author(s)
Nhat-Tan PHAN, Nitin Prasad, Abderrazak Hakam, Ahmed SIDI EL VALLI, Lorena Anghel, Luana Carina Benetti, Advait Madhavan, Alex Jenkins, Ricardo Ferreira, Mark Stiles, Ursula Ebels, Philippe Talatchian
Unbiased sources of true randomness are critical for the successful deployment of stochastic unconventional computing schemes and encryption applications in hardware. Leveraging nanoscale thermal magnetization fluctuations provides an efficient and almost

Experimental demonstration of a robust training method for strongly defective neuromorphic hardware

December 11, 2023
Author(s)
William Borders, Advait Madhavan, Matthew Daniels, Vasileia Georgiou, Martin Lueker-Boden, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland, Brian Hoskins
Neural networks are increasing in scale and sophistication, catalyzing the need for efficient hardware. An inevitability when transferring neural networks to hardware is that non-idealities impact performance. Hardware-aware training, where non-idealities

Breakdown of the drift-diffusion model for transverse spin transport in a disordered Pt film

October 25, 2023
Author(s)
Kirill Belashchenko, Giovanni G. Baez Flores, Wuzhang Fang, Alexei Kovalev, Mark van Schilfgaarde, Mark Stiles, Paul M. Haney
Spin accumulation and spin current profiles are calculated for a disordered Pt film subjected to an in-plane electric current within the nonequilibrium Green function approach. In the bulklike region of the sample, this approach captures intrinsic spin

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

September 28, 2023
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 uniquely suited for unlocking novel computing schemes for in-hardware neuromorphic computing. In this

Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities

July 13, 2023
Author(s)
William Borders, Nitin Prasad, Brian Hoskins, Advait Madhavan, Matthew Daniels, Vasileia Gerogiou, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland
Increasingly higher demand in chip area and power consumption for more sophisticated artificial neural networks has catalyzed efforts to develop architectures, circuits, and devices that perform like the human brain. However, many novel device technologies

Characterization of Noise in CMOS Ring Oscillators at Cryogenic Temperatures

July 12, 2023
Author(s)
Prashansa Mukim, Pragya Shrestha, Advait Madhavan, Nitin Prasad, Jason Campbell, Forrest Brewer, Mark Stiles, Jabez J. McClelland
Allan deviation provides a means to characterize the time-dependence of noise in oscillators and potentially identify the source characteristics. Measurements on a 130nm, 7-stage ring oscillator show that the Allan deviation declines from 300K to 150K as

Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays

May 18, 2023
Author(s)
Siyuan Huang, Brian Hoskins, Matthew Daniels, Mark Stiles, Gina C. Adam
The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, espe- cially on the movement and calculation of gradient information, we introduce

Large Exotic Spin Torques in Antiferromagnetic Iron Rhodium

August 29, 2022
Author(s)
Jonathan Gibbons, Takaaki Dohi, Vivek Amin, Fei Xue, Haowen Ren, Hanu Arava, Hilal Saglam, Yuzi Liu, John Pearson, Nadya Mason, Amanda Petford-Long, Paul M. Haney, Soho Shim, Jun-wen Xu, Mark Stiles, Eric Fullerton, Andrew Kent, Shunsuke Fukami, Axel Hoffmann
Spin torque is a promising tool for driving magnetization dynamics for novel computing techniques. These torques can be easily produced by spin-orbit effects, but for most conventional spin source materials, a high degree of crystal symmetry limits the

Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

July 19, 2022
Author(s)
Axel Hoffmann, Shriram Ramanathan, Julie Grollier, Andrew Kent, Marcelo Rozenberg, Ivan Schuller, Oleg Shpyrko, Robert Dynes, Yeshaiahu Fainman, Alex Frano, Eric Fullerton, Giulia Galli, Vitaliy Lomakin, Shyue Ping Ong, Amanda K. Petford-Long, Jonathan A. Schuller, Mark Stiles, Yayoi Takamura, Yimei Zhu
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device

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

July 18, 2022
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-specific hardware. Avenues to mitigate the main issue, the von Neumann bottleneck, include in

Easy-plane spin Hall nano-oscillators as spiking neurons for neuromorphic computing

January 10, 2022
Author(s)
Danijela Markovic, Matthew Daniels, Pankaj Sethi, Andrew Kent, Mark Stiles, Julie Grollier
We show analytically using a macrospin approximation that easy-plane spin Hall nano-oscillators excited by a spin-current polarized perpendicularly to the easy-plane have phase dynamics analogous to that of Josephson junctions. This allows them to