The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Quantifying the Accuracy of High-Level Fault Injection Techniques for Hardware Faults
Abstract
Hardware errors are on the rise with reducing feature sizes, however tolerating them in hardware is expensive. Researchers have explored software-based techniques for building error resilient applications. Many of these techniques leverage application-specific resilience characteristics to keep overheads low. Understanding application-specific resilience characteristics requires software fault-injection mechanisms that are both accurate and capable of operating at a high-level of abstraction to allow developers to reason about error resilience. In this paper, we quantify the accuracy of high-level software fault injection mechanisms vis-à-vis those that operate at the assembly or machine code levels. To represent high-level injection mechanisms, we built a fault injector tool based on the LLVM compiler, called LLFI. LLFI performs fault injection at the LLVM intermediate code level of the application, which is close to the source code. We quantitatively evaluate the accuracy of LLFI with respect to assembly level fault injection, and understand the reasons for the differences.
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The occurrence of faults may cause an application to crash, hang, or terminate by producing an erroneous result without alert (Silent Data Corruption).
“The occurrence of faults may cause the application: i) to crash or the operating system to raise an exception blocking the execution, ii) to hang leading to a non-termination, or iii) to terminate by producing an erroneous result, without any alert, a.k.a. Silent Data Corruption (SDC) [31].”
From Fast and Accurate Error Simulation for CNNs Against Soft Errors (2023) · cites this paper as [31]
Citations
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Flagged first, then by citations
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- woof, 1995, The splash2 programs: Characterization and methodological considerations, ISCA, 24
- maia, 2002, Xceptiontm-enhanced automated fault-injection environment, DSN, 547
Cited by
Sorted by citations
SASSIFI: An architecture-level fault injection tool for GPU application resilience evaluation
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Self-citationTensorFI: A Flexible Fault Injection Framework for TensorFlow Applications
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Metadata
- Authors
- Wei, J., Thomas, A., Li, G., & Pattabiraman, K.
- Affiliations
- University of British Columbia (CA)
- Venue
- 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 375-382
- ISSN
- 1530-0889 (ISSN-L)
- OpenAlex
- W1965936844
- Cited by
- 182
- References
- 32
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 15 · 2023: 19 · 2022: 16 · 2021: 20 · 2020: 19 · 2019: 22 · 2018: 18 · 2017: 13 · 2016: 17 · 2015: 10 · 2014: 2
- FWCI
- 9.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Dependable Systems & Networks/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radiation Effects in Electronics
- Keywords
- software fault injection, hardware fault injection, error resilience, high-level abstraction, hardware errors, error-tolerant applications
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