Accelerator-friendly neural-network training: Learning variations and defects in RRAM crossbar
Abstract
RRAM crossbar consisting of memristor devices can naturally carry out the matrix-vector multiplication; it thereby has gained a great momentum as a highly energy-efficient accelerator for neuromorphic computing. The resistance variations and stuck-at faults in the memristor devices, however, dramatically degrade not only the chip yield, but also the classification accuracy of the neural-networks running on the RRAM crossbar. Existing hardware-based solutions cause enormous overhead and power consumption, while software-based solutions are less efficient in tolerating stuck-at faults and large variations. In this paper, we propose an accelerator-friendly neural-network training method, by leveraging the inherent self-healing capability of the neural-network, to prevent the large-weight synapses from being mapped to the abnormal memristors based on the fault/variation distribution in the RRAM crossbar. Experimental results show the proposed method can pull the classification accuracy (10%-45% loss in previous works) up close to ideal level with ≤ 1% loss.
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Effective AIMC weight precision is impacted by various nonidealities, including thermal and 1/f noise, randomization during physical switching induced by electrical and thermal fluctuations, material inhomogenities, and device-to-device variability introduced during device fabrication or operation.
“That said, effective AIMC weight precision is impacted by various nonidealities, including thermal and 1/f noise, randomization during physical switching induced by electrical and thermal fluctuations, material inhomogenities30, and device-to-device variability introduced during device fabrication or operation.”
From Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators (2023) · cites this paper as [30]
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Accurate deep neural network inference using computational phase-change memory
Joshi, V., Le Gallo, M., Haefeli, S., Boybat, I., Nandakumar, S. R., Piveteau, C., Dazzi, M., Rajendran, B., Sebastian, A., & Eleftheriou, E. (2020) · Nature Communications · doi:10.1038/s41467-020-16108-9 · Cited by 490 · see citing works
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“That said, effective AIMC weight-precision is impacted by various nonidealities, including thermal and 1/f noise, randomization during physical switching induced by electrical and thermal fluctuations, material inhomogenities [14], and device-to-device variability introduced during device fabrication or operation.”
BackgroundStuck-at Fault Tolerance in RRAM Computing Systems
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Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference
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Nonideality‐Aware Training for Accurate and Robust Low‐Power Memristive Neural Networks
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Meng, Z., Qian, W., Zhao, Y., Sun, Y., Yang, R., & Jiang, L. (2021) · 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) · doi:10.23919/date51398.2021.9474179 · Cited by 10 · see citing works
Self-citationEnergy-Efficient Brain Floating Point Convolutional Neural Network Using Memristors
Tong, S.-Q., Bao, H., Li, J.-C., Yang, L., Zhou, H.-J., Li, Y., & Miao, X.-S. (2024) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2024.3379953 · Cited by 3 · see citing works
Self-citation
- Authors
- Chen, L., Li, J., Chen, Y., Deng, Q., Shen, J., Liang, X., & Jiang, L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- University of Pittsburgh (US)
- Venue
- Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 19-24
- OpenAlex
- W2612375349
- Cited by
- 245
- References
- 3
- Citations by year
- 2026: 16 · 2025: 17 · 2024: 13 · 2023: 35 · 2022: 41 · 2021: 38 · 2020: 45 · 2019: 24 · 2018: 13 · 2017: 2 · 2015: 1
- FWCI
- 32.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Design, Automation, and Test in Europe (publishedVersion)
- 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 › Computer Science › Hardware and Architecture › VLSI and Analog Circuit Testing
- Keywords
- memristors, neural network training, stuck-at faults, resistance variation, neuromorphic accelerators, fault tolerance, classification accuracy, energy-efficient computing
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