22 citations · 27 across the 11 of their papers we have counts for
8 papers
Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
Ojas Parekh, Cynthia A. Phillips, Conrad D. James +1
Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent adv…
Solving a steady-state PDE using spiking networks and neuromorphic hardware
J. Darby Smith, William Severa, Aaron J. Hill +5
The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine lea…
Composing Neural Algorithms with Fugu
James B Aimone, William Severa, Craig M Vineyard
Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, the…
Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication
William Severa, Craig M. Vineyard, Ryan Dellana +2
This paper presents a new technique for training networks for low-precision communication. Targeting minimal communication between nodes not only enables the use of emerging spikin…
Spiking Neural Algorithms for Markov Process Random Walk
William Severa, Rich Lehoucq, Ojas Parekh +1
The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be use…
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
Justin E. Doak, Joe B. Ingram, Sam A. Mulder +6
A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary i…