NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

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.

Artificial intelligence

Hardware for AI

Search Publications

Search Title, Abstract, Conference, Citation, Keyword or Author
  • Published Date
Displaying 1 - 10 of 10

Quasi-Deterministic Channel Propagation Model for Human Sensing: Gesture Recognition Use Case

July 9, 2025
Author(s)
Jack Chuang, Raied Caromi, Jelena Senic, Samuel Berweger, Neeraj Varshney, Jian Wang, Anuraag Bodi, Camillo Gentile, Nada Golmie
We describe a quasi-determinstic channel propagation model for human gesture recognition reduced from real-time measurements with our context aware channel sounder, considering four human subjects and 20 distinct body motions, for a total of 120,000

Strain-programmable van der Waals magnetic tunnel junctions

June 3, 2025
Author(s)
John Cenker, Dmitry Ovchinnikov, Harvey Yang, Daniel Chica, Catherine Zhu, Jiaqi Cai, Geoffrey Diederich, Zhaoyu Liu (liuzhaoyu), Xiaoyang Zhu, Xavier Roy, Ting Cao, Matthew Daniels, Jiun-Haw Chu, Di Xiao, Xiaodong Xu
Magnetic tunnel junctions (MTJs) are foundational spintronics devices with applications ranging from stable magnetic memory to emerging stochastic computing schemes. Integrating van der Waals magnets into these devices could enable the realization of

Layer ensemble averaging for fault tolerance in memristive neural networks

February 1, 2025
Author(s)
Osama Yousuf, Brian Hoskins, Karthick Ramu, Mitchell Fream, William Borders, Advait Madhavan, Matthew Daniels, Andrew Dienstfrey, Jabez McClelland, Martin Lueker-Boden, Gina Adam
Advancements in continual learning with artificial neural networks have been fueled in large part by scaling network dimensionalities. As this scaling continues, conventional computing systems are becoming increasingly inefficient due to the von Neumann

Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud

August 2, 2024
Author(s)
Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Hang Mi, Bo Ai, Ruisi He
Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in

Modeling Spiking Neurons without Spikes

August 2, 2024
Author(s)
Jeff Shainline, Bryce Primavera, Ryan O'Loughlin
While spiking neuromorphic hardware holds promise for efficient implementations of artificial intelligence, the impact has been limited due in part to a lack of learning algorithms that achieve performance superior to conventional deep learning. One

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

AI-Based Environment Segmentation Using a Context-Aware Channel Sounder

April 26, 2024
Author(s)
Anuraag Bodi, Samuel Berweger, Raied Caromi, Jihoon Bang, Jelena Senic, Camillo Gentile
We describe how the data acquired from the camera and Lidar systems of our context-aware radio-frequency (RF) channel sounder is used to reconstruct a 3D mesh of the surrounding environment, segmented and classified into discrete objects. First, the images

Context-Aware Channel Sounder for AI-Assisted Radio-Frequency Channel Modeling

April 26, 2024
Author(s)
Camillo Gentile, Jelena Senic, Anuraag Bodi, Samuel Berweger, Raied Caromi, Nada Golmie
We describe a context-aware channel sounder that consists of three separate systems: a radio-frequency system to extract multipaths scattered from the surrounding environment in the 3D geometrical domain, a Lidar system to generate a point cloud of the

Device Modeling Bias in ReRAM-Based Neural Network Simulations

January 20, 2023
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 realistic models of the device population. Jump table modeling has emerged as a phenomenological
Was this page helpful?