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Impact of Non-Ideal Characteristics of Resistive Synaptic Devices on Implementing Convolutional Neural Networks
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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“Due to fabrication imperfections, non-ideal behaviors are observed in memristor and memcapacitor devices. These include variations in conductance, capacitance, device-todevice (D2D), and cycle-to-cycle (C2C), as well as nonlinearity and programming failure [28].”
From Framework for In-Memory Computing Based on Memristor and Memcapacitor for On-Chip Training (2023) · cites this paper as [28]
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Metadata
- Authors
- Sun, X., & Yu, S.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- IEEE Journal on Emerging and Selected Topics in Circuits and Systems
- Published
- 2019-09
- Volume
- 9
- Issue
- 3
- Pages
- 570-579
- ISSN
- 2156-3357 (ISSN-L)
- 2156-3365
- 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
- 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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