Energy Efficient Learning With Low Resolution Stochastic Domain Wall Synapse for Deep Neural Networks
Author:
Affiliation:
1. Mechanical and Nuclear Engineering Department, Virginia Commonwealth University, Richmond, VA, USA
2. CNRS, Centre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, Palaiseau, France
Funder
National Science Foundation
Virginia Commonwealth Cyber Initiative (CCI) CCI Cybersecurity Research Collaboration Grant
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
https://ieeexplore.ieee.org/ielam/6287639/9668973/9850990-aam.pdf
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4. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning;zhang;Proc 34th Int Conf Mach Learn,2017
5. Binaryconnect: Training deep neural networks with binary weights during propagations;courbariaux;Proc 28th Int Conf Neural Inf Process Syst,2015
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