3 citations · 4 across the 3 of their papers we have counts for
5 papers
Device-aware inference operations in SONOS nonvolatile memory arrays
Christopher H. Bennett, T. Patrick Xiao, Ryan Dellana +10
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference. However, these systems are affected by device-level noise and retention issues. Here, we…
Evaluating complexity and resilience trade-offs in emerging memory inference machines
Christopher H. Bennett, Ryan Dellana, T. Patrick Xiao +6
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of po…
PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM
Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +7
The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Numerous special-purp…
Using Floating Gate Memory to Train Ideal Accuracy Neural Networks
Sapan Agarwal, Diana Garland, John Niroula +7
Floating gate SONOS (Silicon-Oxygen-Nitrogen-Oxygen-Silicon) transistors can be used to train neural networks to ideal accuracies that match those of floating point digital weights…
Probability Series Expansion Classifier that is Interpretable by Design
Sapan Agarwal, Corey M. Hudson
This work presents a new classifier that is specifically designed to be fully interpretable. This technique determines the probability of a class outcome, based directly on probabi…