no code implementations • 18 Oct 2022 • Ethan Ancell, Christopher Bennett, Bert Debusschere, Sapan Agarwal, Park Hays, T. Patrick Xiao
Bayesian neural networks (BNNs) are an important type of neural network with built-in capability for quantifying uncertainty.
Generative Adversarial Network Out of Distribution (OOD) Detection +1
no code implementations • 22 Nov 2021 • Thomas Leonard, Samuel Liu, Mahshid Alamdar, Can Cui, Otitoaleke G. Akinola, Lin Xue, T. Patrick Xiao, Joseph S. Friedman, Matthew J. Marinella, Christopher H. Bennett, Jean Anne C. Incorvia
In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain.
no code implementations • 3 Sep 2021 • T. Patrick Xiao, Ben Feinberg, Christopher H. Bennett, Venkatraman Prabhakar, Prashant Saxena, Vineet Agrawal, Sapan Agarwal, Matthew J. Marinella
Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference.
no code implementations • 2 Apr 2020 • Christopher H. Bennett, T. Patrick Xiao, Ryan Dellana, Vineet Agrawal, Ben Feinberg, Venkatraman Prabhakar, Krishnaswamy Ramkumar, Long Hinh, Swatilekha Saha, Vijay Raghavan, Ramesh Chettuvetty, Sapan Agarwal, Matthew J. Marinella
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference.
no code implementations • 4 Mar 2020 • Christopher H. Bennett, T. Patrick Xiao, Can Cui, Naimul Hassan, Otitoaleke G. Akinola, Jean Anne C. Incorvia, Alvaro Velasquez, Joseph S. Friedman, Matthew J. Marinella
Machine learning implements backpropagation via abundant training samples.
no code implementations • 25 Feb 2020 • Christopher H. Bennett, Ryan Dellana, T. Patrick Xiao, Ben Feinberg, Sapan Agarwal, Suma Cardwell, Matthew J. Marinella, William Severa, Brad Aimone
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 potential disturbance.