Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks-Reference-Cited by-同舟云学术

Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks

Published:2022-03-14 Issue: Volume: Page:
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Container-title:2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Bhattacharjee Abhiroop¹,Bhatnagar Lakshya²,Panda Priyadarshini¹

Affiliation:

1. Yale University,Department of Electrical Engineering,USA

2. Indian Institute of Technology,Delhi,India

Funder

C-BRIC, a JUMP center sponsored by DARPA and SRC, the National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9774496/9774497/09774736.pdf?arnumber=9774736

Reference15 articles.

1. NEAT: Non-linearity Aware Training for Accurate, Energy-Efficient and Robust Implementation of Neural Networks on 1T-1R Crossbars

2. GENIEx: A Generalized Approach to Emulating Non-Ideality in Memristive Xbars using Neural Networks

3. Reduction and IR-drop compensations techniques for reliable neuromorphic computing systems

4. Switchx: Gmin-gmax switching for energy-efficient and robust implementation of binary neural networks on reram xbars;bhattacharjee;ArXiv Preprint,2021

5. The lottery ticket hypothesis: Finding sparse, trainable neural networks;frankle;ArXiv Preprint,2018

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