5 papers
Incorruptible Neural Networks: Training Models that can Generalize to Large Internal Perturbations
Philip Jacobson, Ben Feinberg, Suhas Kumar +3
Flat regions of the neural network loss landscape have long been hypothesized to correlate with better generalization properties. A closely related but distinct problem is training…
Strong radial electric field scaling near nanoscale conductive filaments and the ReRAM resistive switching mechanism
Robin Jacobs-Gedrim, William Wahby, Thomas Awe +10
The physics underlying reset in bipolar resistive memory has been the subject of decades of controversy and has been identified as the primary barrier to resistive memory technolog…
Analog Bayesian neural networks are insensitive to the shape of the weight distribution
Ravi G. Patel, T. Patrick Xiao, Sapan Agarwal +1
Recent work has demonstrated that Bayesian neural networks (BNN's) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of m…
Analog fast Fourier transforms for scalable and efficient signal processing
T. Patrick Xiao, Ben Feinberg, David K. Richardson +7
Edge devices are being deployed at increasing volumes to sense and act on information from the physical world. The discrete Fourier transform (DFT) is often necessary to make this…
TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives
Ryan Wong, Nikita Kim, Kevin Higgs +4
As the amount of data produced in society continues to grow at an exponential rate, modern applications are incurring significant performance and energy penalties due to high data…