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Arithmetic-intensity-guided fault tolerance for neural network inference on GPUs
Open accessAbstract
Neural networks (NNs) are increasingly employed in safety-critical domains and in environments prone to unreliability (e.g., soft errors), such as on spacecraft. Therefore, it is critical to impart fault tolerance to NN inference. Algorithm-based fault tolerance (ABFT) is emerging as an efficient approach for fault tolerance in NNs.
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“Particularly, we want to emphasize that standard ABFT is widely utilized and can still be considered to be one of the state-of-the-art algorithm-based fault tolerance approaches.”
From Soft Error Reliability Analysis of Vision Transformers (2023) · cites this paper as [40]
In the citing paper
“They conduct ABFT with the granularity of GEMMs and can only recover from a single computing error.”
From Soft Error Reliability Analysis of Vision Transformers (2023) · cites this paper as [40]
In the citing paper
“Despite the different application scenarios of ABFT, [40] [52]-[57] essentially utilize standard ABFT approaches.”
From Soft Error Reliability Analysis of Vision Transformers (2023) · cites this paper as [40]
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Cited by
Sorted by citations
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
“Algorithm-based fault-tolerance (ABFT) techniques [37], [38], [39], [40], originally developed for matrix–matrix multiplication based on checksum, have also been explored to detect and recover faults in deep learning without model modification, which are particularly suitable for…”
BackgroundInfluentialLow-Voltage Energy Efficient Neural Inference by Leveraging Fault Detection Techniques
Safarpour, M., Deng, T. Z., Massingham, J., Xun, L., Sabokrou, M., & Silven, O. (2021) · 2021 IEEE Nordic Circuits and Systems Conference (NorCAS) · doi:10.1109/norcas53631.2021.9599648 · Cited by 12
Reliability 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
“These include the ECC memory protection mechanism, software duplication, or different ABFT strategies applied to different kernels and layers, such as matrix multiplication or convolution [32], [33], [34].”
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
Metadata
- Authors
- Kosaian, J., & Rashmi, K. V.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
- Publisher
- ACM
- Published
- 2021-11-13
- Pages
- 1-15
- ISSN
- 2167-4337 (ISSN-L)
- OpenAlex
- W3156988338
- Cited by
- 53
- References
- 4
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 15 · 2023: 10 · 2022: 4 · 2021: 4
- FWCI
- 7.63
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Conference for High Performance Computing, Networking, Storage and Analysis (SC) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Regional Development Fund (AIDA -- Adaptive, Intelligent and Distributed Assurance Platform -- project (reference POCI-01-0247-FEDER-045907) co-financed by the ERDF - European Regional Development Fund through the Operational Program for Competitiveness and Internationalisation)
- Amazon Web Services
- National Science Foundation (DGE-1745016 and DGE-1252522)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radiation Effects in Electronics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- fault tolerance, neural network inference, GPU computing, arithmetic intensity, algorithm-based fault tolerance, soft errors, safety-critical systems, spacecraft
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- Kosaian, J., & Rashmi, K. V. (2021). Arithmetic-intensity-guided fault tolerance for neural network inference on GPUs. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. https://doi.org/10.1145/3458817.3476184
