13 citations · 21 across the 8 of their papers we have counts for
10 papers
Layer Ensemble Averaging for Improving Memristor-Based Artificial Neural Network Performance
Osama Yousuf, Brian Hoskins, Karthick Ramu +8
Artificial neural networks have advanced due to scaling dimensions, but conventional computing faces inefficiency due to the von Neumann bottleneck. In-memory computation architect…
Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation
Adam N. McCaughan, Bakhrom G. Oripov, Natesh Ganesh +3
We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware. MGD utilizes zero-order optimiza…
Device Modeling Bias in ReRAM-based Neural Network Simulations
Osama Yousuf, Imtiaz Hossen, Matthew W. Daniels +3
Data-driven modeling approaches such as jump tables are promising techniques to model populations of resistive random-access memory (ReRAM) or other emerging memory devices for har…
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing
Zachary Grey, Susanna Mosleh, Jacob Rezac +3
Radio spectrum is a high-demand finite resource. To meet growing demands of data throughput for forthcoming and deployed wireless networks, new wireless technologies must operate i…
Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing
Zachary J. Grey, Susanna Mosleh, Jacob D. Rezac +3
To meet the ever-growing demands of data throughput for forthcoming and deployed wireless networks, new wireless technologies like Long-Term Evolution License-Assisted Access (LTE-…
Towards a priori uncertainty quantification in coarse-grained molecular dynamics: Generalized multipole potentials
Paul N. Patrone, Andrew M. Dienstfrey, Geoffrey B. McFadden
In computational materials science, coarse-graining approaches often lack a priori uncertainty quantification (UQ) tools that estimate the accuracy of a reduced-order model before…