CRUS: A Hardware-Efficient Algorithm Mitigating Highly Nonlinear Weight Update in CIM Crossbar Arrays for Artificial Neural Networks-Reference-Cited by-同舟云学术

CRUS: A Hardware-Efficient Algorithm Mitigating Highly Nonlinear Weight Update in CIM Crossbar Arrays for Artificial Neural Networks

Published:2022-12 Issue:2 Volume:8 Page:145-154
ISSN:2329-9231
Container-title:IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
language:
Short-container-title:IEEE J. Explor. Solid-State Comput. Devices Circuits

Author:

Lee Junmo¹^ORCID,Hwang Joon²^ORCID,Cho Youngwoon³,Park Min-Kyu²^ORCID,Choi Woo Young²^ORCID,Kim Sangbum³^ORCID,Lee Jong-Ho²^ORCID

Affiliation:

1. Department of ECE, Georgia Institute of Technology, Atlanta, GA, USA

2. Department of ECE and ISRC, Seoul National University, Seoul, South Korea

3. Department of MSE and ISRC, Seoul National University, Seoul, South Korea

Funder

Ministry of Science and ICT, South Korea

Ministry of Trade, Industry & Energy

Brain Korea 21 Plus Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Electronic, Optical and Magnetic Materials

Link

Reference43 articles.

4. On the noisy gradient descent that generalizes as sgd;wu;Proc Int Conf Mach Learn,2020

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