A survey on modeling and improving reliability of DNN algorithms and accelerators
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Model compression techniques can weaken fault-resilience and adversarial-robustness of a model by removing parameters that are unimportant for normal execution but useful against perturbed input data or faulty weights.
“The model compression techniques can weaken fault-resilience and adversarial-robustness of a model by removing parameters that are unimportant for normal execution but are useful against perturbed input data or faulty weights [269, 270].”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [269]
Cites
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357 · see citing works
Analyzing and Increasing the Reliability of Convolutional Neural Networks on GPUs
Santos, F. F. D., Pimenta, P. F., Lunardi, C., Draghetti, L., Carro, L., Kaeli, D., & Rech, P. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2878387 · Cited by 226 · see citing works
Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference
Rekhi, A. S., Zimmer, B., Nedovic, N., Liu, N., Venkatesan, R., Wang, M., Khailany, B., Dally, W. J., & Gray, C. T. (2019) · Proceedings of the 56th Annual Design Automation Conference 2019 · doi:10.1145/3316781.3317770 · Cited by 67 · see citing works
Flagged first, then by citations
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A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154 · see citing works
Self-citationSoft errors in DNN accelerators: A comprehensive review
Ibrahim, Y., Wang, H., Liu, J., Wei, J., Chen, L., Rech, P., Adam, K., & Guo, G. (2020) · Microelectronics Reliability · doi:10.1016/j.microrel.2020.113969 · Cited by 102 · see citing works
Afandizadeh, S., Abdolahi, S., & Mirzahossein, H. (2024) · Journal of Advanced Transportation · doi:10.1155/2024/9981657 · Cited by 64 · see citing works
A survey on hardware security of DNN models and accelerators
Mittal, S., Gupta, H., & Srivastava, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102163 · Cited by 47 · see citing works
Self-citationLi, Y., Cao, Y., Yang, J., Wu, M., Yang, A., & Li, J. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.04.026 · Cited by 43 · see citing works
Soft Error Reliability Analysis of Vision Transformers
Xue, X., Liu, C., Wang, Y., Yang, B., Luo, T., Zhang, L., Li, H., & Li, X. (2023) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2023.3317138 · Cited by 35 · see citing works
“More reliability evaluation study of deep learning can be found in recent surveys [15], [36].”
BackgroundReliability of Vision Transformers and CNNs on Edge AI Systems Under Neutron Radiation
Badia, J. M., Martin-Salinas, I., Leon, G., Amor-Martin, A., Frias-Dominguez, L., Belloch, J. A., Garcia-Valderas, M., Lindoso, A., Cazzaniga, C., & Entrena, L. (2025) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2025.3536519 · Cited by 11 · see citing works
“Several surveys summarize, analyze, and categorize many of these works [8], [11], [23], [24], [25], [26].”
Winograd convolution: a perspective from fault tolerance
Xue, X., Huang, H., Liu, C., Luo, T., Zhang, L., & Wang, Y. (2022) · Proceedings of the 59th ACM/IEEE Design Automation Conference · doi:10.1145/3489517.3530531 · Cited by 10 · see citing works
Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors
He, J., Liu, Y., Xu, C., Liao, X., & Yang, Y. (2025) · Electronics · doi:10.3390/electronics14122418 · Cited by 4 · see citing works
- Authors
- Mittal, S.
- Affiliations
- Indian Institute of Technology Hyderabad (IN)
- Corresponding author
- Mittal, Sparsh
- ORCID
- Mittal, Sparsh
- Venue
- Journal of Systems Architecture
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 104
- Pages
- 101689
- ISSN
- 1383-7621 (ISSN-L)
- OpenAlex
- W2989569745
- Cited by
- 175
- References
- 3
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 39 · 2023: 30 · 2022: 21 · 2021: 20 · 2020: 9
- FWCI
- 4.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Systems Architecture (publishedVersion)
- Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) (acceptedVersion)
- Funders
- Semiconductor Research Corporation
- Science and Engineering Research Board
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radiation Effects in Electronics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- deep neural network accelerators, soft errors, hard errors, process variation, voltage scaling, timing errors, thermal effects, fault tolerance, reliability modeling, reliability optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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