The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Noise-Resilient DNN: Tolerating Noise in PCM-Based AI Accelerators via Noise-Aware Training
Abstract
Phase change memory (PCM)-based “Analog-AI” accelerators are gaining importance for inference in edge applications due to the energy efficiency offered by in-memory computing. Nevertheless, noise sources inherent to PCM devices cause inaccuracies in the deep neural network (DNN) weight values. Such inaccuracies can lead to severe degradation in model accuracy. To address this, we propose two techniques to improve noise resiliency of DNNs: 1) drift regularization (DR) and 2) multiplicative noise training (MNT). We evaluate convolutional networks trained on image classification and recurrent neural networks trained on language modeling and show that our techniques improve model accuracy by up to 12% over one month.
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“A number of previous studies have shown that noise-aware training —simple injection of noise onto weights or activations during DNN training—can make DNNs more robust for AIMC deployment33,38–42.”
From Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators (2023) · cites this paper as [41]
Citations
Cites
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In-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298
Equivalent-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
Self-citationNeuromorphic computing with multi-memristive synapses
Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 899
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
Tsai, H., Ambrogio, S., Mackin, C., Narayanan, P., Shelby, R. M., Rocki, K., Chen, A., & Burr, G. W. (2019) · 2019 Symposium on VLSI Technology · doi:10.23919/vlsit.2019.8776519 · Cited by 47
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Cited by
Sorted by citations
An analog-AI chip for energy-efficient speech recognition and transcription
Ambrogio, S., Narayanan, P., Okazaki, A., Fasoli, A., Mackin, C., Hosokawa, K., Nomura, A., Yasuda, T., Chen, A., Friz, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06337-5 · Cited by 269
“Furthermore, blocks 1(−1), 9(−9) and 2(−2), 10(−10) of Enc-LSTM0 Wx and Enc-LSTM1 Wx, and blocks 1(9), 17(25) (WP1(WP2)) and 2(10), 18(26) were summed in digital after on-chip analog MAC.”
MethodSelf-citationRasch, M. J., Mackin, C., Le Gallo, M., Chen, A., Fasoli, A., Odermatt, F., Li, N., Nandakumar, S. R., Narayanan, P., Tsai, H., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40770-4 · Cited by 213
“[36, 24, 38]), and has been one of the hallmarks of HWA training for AIMC.”
BackgroundMethodInfluentialSelf-citationNarayanan, P., Ambrogio, S., Okazaki, A., Hosokawa, K., Tsai, H., Nomura, A., Yasuda, T., Mackin, C., Lewis, S. C., Friz, A., et al. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3115993 · Cited by 85
Self-citation
Metadata
- Authors
- Kariyappa, S., Tsai, H., Spoon, K., Ambrogio, S., Narayanan, P., Mackin, C., Chen, A., Qureshi, M., & Burr, G. W.
- Affiliations
- Georgia Institute of Technology (US)
- IBM Research - Almaden (US)
- ORCID
- Venue
- IEEE Transactions on Electron Devices
- Published
- 2021-09
- Volume
- 68
- Issue
- 9
- Pages
- 4356-4362
- ISSN
- 0018-9383 (ISSN-L)
- 1557-9646
- OpenAlex
- W3195461290
- Cited by
- 45
- References
- 6
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 9 · 2023: 13 · 2022: 9 · 2021: 2
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Electron Devices (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- phase-change memory, noise robustness, noise-aware training, in-memory computing, edge inference, convolutional neural networks, recurrent neural networks, device noise
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