Cited by 170Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesAdvanced Neural Network Applications

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Impact of Non-Ideal Characteristics of Resistive Synaptic Devices on Implementing Convolutional Neural Networks

Sun, Xiaoyu, Yu, Shimeng·2019·IEEE Journal on Emerging and Selected Topics in Circuits and Systems

DOI 10.1109/jetcas.2019.2933148

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Abstract

Emerging non-volatile memory (eNVM) based resistive synaptic devices have shown great potential for implementing deep neural networks (DNNs). However, the eNVM devices typically suffer from various non-ideal effects which may degrade the performance of the system. Based on a representative convolutional neural network (CNN) model for CIFAR-10 dataset, this paper comprehensively investigates the impact of those nonideal characteristics, such as nonlinearity and asymmetry of conductance tuning, variations, endurance and retention, on the training/inference accuracy. The compact models of the device non-ideal effects are incorporated into the TensorFlow framework. Our simulation results suggest that 1) the training accuracy is more sensitive to the asymmetry of conductance tuning than the nonlinearity; 2) the conductance range variation does not degrade the training accuracy, instead, a small variation can even reduce the accuracy loss introduced by asymmetry; 3) device-to-device variation can also remedy the accuracy loss due to asymmetry while cycle-to-cycle variation leads to significant accuracy degradation; 4) the accuracy degradation will not be noticeable if the endurance cycles are more than 7,000 cycles; and 5) different drifting modes affect the inference accuracy differently, and the best case is where the conductance is drifting up/down randomly.

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Authors: Sun, X., & Yu, S.
Affiliations: Georgia Institute of Technology (US)
ORCID: Sun, Xiaoyu
Yu, Shimeng
Venue: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-09
Volume: 9
Issue: 3
Pages: 570-579
ISSN: 2156-3357 (ISSN-L)
2156-3365
DOI: 10.1109/jetcas.2019.2933148
OpenAlex: W2966199719
Cited by: 170
References: 40
Citations by year: 2026: 9 · 2025: 25 · 2024: 34 · 2023: 31 · 2022: 25 · 2021: 22 · 2020: 22 · 2019: 2
FWCI: 6.38
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (am)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Journal on Emerging and Selected Topics in Circuits and Systems (publishedVersion)
Funders: National Science Foundation (CCF-1903951)
Defense Advanced Research Projects Agency
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: non-volatile memory, convolutional neural networks, device-to-device variation, cycle-to-cycle variation, endurance, retention, conductance drift, training accuracy, inference accuracy

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Impact of Non-Ideal Characteristics of Resistive Synaptic Devices on Implementing Convolutional Neural Networks

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