Energy Efficient Learning With Low Resolution Stochastic Domain Wall Synapse for Deep Neural Networks

Author:

Misba Walid Al1ORCID,Lozano Mark1,Querlioz Damien2,Atulasimha Jayasimha1ORCID

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

Reference57 articles.

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2. Mixed-Precision Deep Learning Based on Computational Memory

3. DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients;zhou;arXiv 1606 06160 [cs],2016

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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