Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913608035729408 |
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| author | Esposito, Giuseppe Guerrero-Balaguera, Juan David Condia, Josie Esteban Rodriguez Reorda, Matteo Sonza |
| author_facet | Esposito, Giuseppe Guerrero-Balaguera, Juan David Condia, Josie Esteban Rodriguez Reorda, Matteo Sonza |
| contents | The reliability of Neural Networks has gained significant attention, prompting efforts to develop SW-based hardening techniques for safety-critical scenarios. However, evaluating hardening techniques using application-level fault injection (FI) strategies, which are commonly hardware-agnostic, may yield misleading results. This study for the first time compares two FI approaches (at the application level (APP) and instruction level (ISA)) to evaluate deep neural network SW hardening strategies. Results show that injecting permanent faults at ISA (a more detailed abstraction level than APP) changes completely the ranking of SW hardening techniques, in terms of both reliability and accuracy. These results highlight the relevance of using an adequate analysis abstraction for evaluating such techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Esposito, Giuseppe Guerrero-Balaguera, Juan David Condia, Josie Esteban Rodriguez Reorda, Matteo Sonza Neural and Evolutionary Computing The reliability of Neural Networks has gained significant attention, prompting efforts to develop SW-based hardening techniques for safety-critical scenarios. However, evaluating hardening techniques using application-level fault injection (FI) strategies, which are commonly hardware-agnostic, may yield misleading results. This study for the first time compares two FI approaches (at the application level (APP) and instruction level (ISA)) to evaluate deep neural network SW hardening strategies. Results show that injecting permanent faults at ISA (a more detailed abstraction level than APP) changes completely the ranking of SW hardening techniques, in terms of both reliability and accuracy. These results highlight the relevance of using an adequate analysis abstraction for evaluating such techniques. |
| title | Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2412.08466 |