The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
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]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
Chen, Y.-H., Krishna, T., Emer, J. S., & Sze, V. (2017) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2016.2616357 · Cited by 3,247
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1602.02830 · Cited by 2,106
Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
Kuzum, D., Jeyasingh, R. G. D., Lee, B., & Wong, H.-S. P. (2012) · Nano Letters · doi:10.1021/nl201040y · Cited by 1,267 · cited 3×
“In recent years, emerging non-volatile memory (eNVM) based resistive synaptic devices [5], such as phase change memory (PCM) [6], [7], resistive random access memory (RRAM) [8]–[11], and ferroelectric field effect transistor (FeFET) [12], [13] are considered as promising candidates to replace SRAM cells due to the sma…”
BackgroundNeuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Self-citationEquivalent-accuracy accelerated neural-network training using analogue memory
Ambrogio, S., Narayanan, P., Tsai, H., Shelby, R. M., Boybat, I., di Nolfo, C., Sidler, S., Giordano, M., Bodini, M., Farinha, N. C. P., et al. (2018) · Nature · doi:10.1038/s41586-018-0180-5 · Cited by 1,151
Burr, G. W., Shelby, R. M., Sidler, S., di Nolfo, C., Jang, J., Boybat, I., Shenoy, R. S., Narayanan, P., Virwani, K., Giacometti, E. U., et al. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2439635 · Cited by 936
Cited by
Sorted by citations
Hardware implementation of memristor-based artificial neural networks
Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45670-9 · Cited by 564
“The same philosophy was adopted by Sun et al. 229 , placing special emphasis on the effect of the non-linear and quantized nature of the synaptic weight update.”
Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospects
Yu, S., Jiang, H., Huang, S., Peng, X., & Lu, A. (2021) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2021.3092533 · Cited by 414
Self-citationRecent Advances and Future Prospects for Memristive Materials, Devices, and Systems
Song, M., Kang, J., Zhang, X., Ji, W., Ascoli, A., Messaris, I., Demirkol, A. Ş., Dong, B., Aggarwal, S., Wan, W., et al. (2023) · ACS Nano · doi:10.1021/acsnano.3c03505 · Cited by 324
RRAM for Compute-in-Memory: From Inference to Training
Yu, S., Shim, W., Peng, X., & Luo, Y. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3072200 · Cited by 149
Self-citationChakraborty, I., Ali, M., Ankit, A., Jain, S., Roy, S., Sridharan, S., Agrawal, A., Raghunathan, A., & Roy, K. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3003007 · Cited by 140
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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