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 • 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 • 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.
1 code implementation • 27 Oct 2017 • Sapan Agarwal, Corey M. Hudson
This work presents a new classifier that is specifically designed to be fully interpretable.
no code implementations • 31 Jul 2017 • Matthew J. Marinella, Sapan Agarwal, Alexander Hsia, Isaac Richter, Robin Jacobs-Gedrim, John Niroula, Steven J. Plimpton, Engin Ipek, Conrad D. James
A detailed circuit and device-level analysis of energy, latency, area, and accuracy are given and compared to relevant designs using standard digital ReRAM and SRAM operations.