Nonideality‐Aware Training for Accurate and Robust Low‐Power Memristive Neural Networks
Open accessAbstract
Abstract Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever‐increasing computing requirements of these structures have contributed to a desire for novel technologies and paradigms, including memristor‐based hardware accelerators. Solutions based on memristive crossbars and analog data processing promise to improve the overall energy efficiency. However, memristor nonidealities can lead to the degradation of neural network accuracy, while the attempts to mitigate these negative effects often introduce design trade‐offs, such as those between power and reliability. In this work, authors design nonideality‐aware training of memristor‐based neural networks capable of dealing with the most common device nonidealities. The feasibility of using high‐resistance devices that exhibit high I‐V nonlinearity is demonstrated—by analyzing experimental data and employing nonideality‐aware training, it is estimated that the energy efficiency of memristive vector‐matrix multipliers is improved by almost three orders of magnitude (0.715 TOPs−1W−1 to 381 TOPs−1W−1) while maintaining similar accuracy. It is shown that associating the parameters of neural networks with individual memristors allows to bias these devices toward less conductive states through regularization of the corresponding optimization problem, while modifying the validation procedure leads to more reliable estimates of performance. The authors demonstrate the universality and robustness of this approach when dealing with a wide range of nonidealities.
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Training and inference must be performed considering that the weight is a function of the input voltage corresponding to the non-Ohmic behavior of devices.
“Instead, training and inference must be performed considering that the weight is a function of the input voltage corresponding to the non-Ohmic behavior of devices40.”
From Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis (2025) · cites this paper as [40]
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Self-citationCompact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks
Lepri, N., Glukhov, A., Mannocci, P., Porzani, M., & Ielmini, D. (2024) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2024.3360015 · Cited by 10 · see citing works
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Physical mechanisms and integration design of memristors
Wang, M., Wang, K., Sun, B. T., Zhou, G., Cao, Z., Gao, K., Ren, F., Chen, X., Li, X., & Shao, J. (2025) · Materials Today Nano · doi:10.1016/j.mtnano.2025.100628 · Cited by 9 · see citing works
On the Reliability of RRAM-Based Neural Networks
Aziza, H., Zambelli, C., Hamdioui, S., Diware, S., Bishnoi, R., & Gebregiorgis, A. (2023) · IFIP/IEEE International Conference on Very Large Scale Integration · doi:10.1109/vlsi-soc57769.2023.10321859 · Cited by 7 · see citing works
Phosphorylation Enables Nano‐Graphene for Tunable Artificial Synapses
Zhang, Z., Qu, Y., Chen, S., Ke, S., Hao, M., Xiao, Y., Zhang, S., Cheng, Z., Xiao, J., Huang, H., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202416794 · Cited by 7 · see citing works
Dynamic Detection and Mitigation of Read-disturb for Accurate Memristor-based Neural Networks
Diware, S., Yaldagard, M. A., Gebregiorgis, A., Joshi, R., Hamdioui, S., & Bishnoi, R. (2024) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas59952.2024.10595966 · Cited by 7 · see citing works
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Difficulties and approaches in enabling learning-in-memory using crossbar arrays of memristors
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Nonideality-aware training makes memristive networks more robust to adversarial attacks
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Genetic Algorithm-Based Training Method for Memristor Neural Networks
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Parameter Extraction of Memristor Model Based on Improved Particle Swarm Optimization
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Spiking Neural Networks with Nonidealities from Memristive Silicon Oxide Devices
Vu, V. C., Kenyon, A. J., Joksas, D., Mehonić, A., Mannion, D. J., & Ng, W. H. (2024) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano61778.2024.10628612 · Cited by 2 · see citing works
Self-citationCMOS‐Compatible Protonic Three‐Terminal Memristor for Analog Synapse in Neuromorphic Computing
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Algorithmic Optimisation for Memristive Deep Learning Accelerators
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Spiking Neural Network with Silicon Oxide Memristive Devices in the Subthreshold Regime
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Self-citationAn Efficient On-chip Adaptation Technique for MAML models on Memristive Hardware
Fida, A. A., & Mittal, S. (2026) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas66217.2026.11562824
Digital-analog concept for superconducting perceptron-like neural networks
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Single event transient effects and variability in hybrid RRAM-CMOS spiking neural network
Nivetha, T., Bindu, B., & Kamsani, N. A. (2026) · Microelectronics Reliability · doi:10.1016/j.microrel.2026.116326
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- Authors
- Joksas, D., Wang, E., Barmpatsalos, N., Ng, W. H., Kenyon, A. J., Constantinides, G. A., & Mehonic, A.
- Affiliations
- University College London (GB)
- Imperial College London (GB)
- Department of Electronic and Electrical Engineering University College London London WC1E 7JE UK
- Corresponding author
- Joksas, Dovydas
- ORCID
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2022-05-04
- Volume
- 9
- Issue
- 17
- Pages
- e2105784-e2105784
- ISSN
- 2198-3844 (ISSN-L)
- PMC
- PMC9189678
- OpenAlex
- W4226247444
- Cited by
- 39
- References
- 44
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 10 · 2023: 9 · 2022: 6
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advanced Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Leverhulme Trust ((RPG-2016-135)
- Royal Academy of Engineering (2094654)
- Imperial College London
- University of Massachusetts Amherst
- Engineering and Physical Sciences Research Council (2094654, EP/S030069/1, EP/P013503/1, EP/L016796/1, EP/P013503)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- memristive neural networks, memristor crossbar, energy efficiency, matrix-vector multiplication, regularization, device reliability, hardware accelerators
- MeSH terms
- Computers, Electric Conductivity, Reproducibility of Results, Neural Networks, Computer
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