Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | LCTES 2021 |
| Untertitel | Proceedings of the 22nd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems |
| Herausgeber/-innen | Jorg Henkel, Xu Liu |
| Seiten | 97-109 |
| Seitenumfang | 13 |
| ISBN (elektronisch) | 978-1-4503-8472-8 |
| Publikationsstatus | Veröffentlicht - 22 Juni 2021 |
| Veranstaltung | 22nd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems - online Dauer: 22 Juni 2021 → … Konferenznummer: 22 |
Publikationsreihe
| Name | Proceedings of the ACM SIGPLAN Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES) |
|---|
Abstract
We describe data-flow pruning, a new data-flow sensitive fault-space pruning method that extends on def/use-pruning by also considering the instructions’ semantics when deriv- ing fault-equivalence sets. By tracking the information flow for each bit individually across the respective instructions and considering their fault-masking capability, data-flow pruning (DFP) has to plan fewer pilot injections as it derives larger fault-equivalence sets. Like def/use pruning, DFP is precise and complete and it can be used as a direct replace- ment/alternative in existing software-based fault-injection tools. Our prototypical implementation so far considers lo- cal fault equivalence for five types of instructions. In our experimental evaluation, this already reduces the number of necessary injections by up to 18 percent compared to def/use pruning.
ASJC Scopus Sachgebiete
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- Software
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LCTES 2021: Proceedings of the 22nd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems. Hrsg. / Jorg Henkel; Xu Liu. 2021. S. 97-109 (Proceedings of the ACM SIGPLAN Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES)).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Data-flow-sensitive fault-space pruning for the injection of transient hardware faults
AU - Pusz, Oskar
AU - Dietrich, Christian
AU - Lohmann, Daniel
N1 - Conference code: 22
PY - 2021/6/22
Y1 - 2021/6/22
N2 - In the domain of safety-critical systems, fault injection cam- paigns on ISA-level have become a widespread approach to systematically assess the resilience of a system with respect to transient hardware faults. However, experimentally in- jecting all possible faults to achieve full fault-space coverage is infeasible in practice. Hence, pruning techniques, such as def/use pruning are commonly applied to reduce the cam- paign size by grouping injections that surely provoke the same erroneous behavior.We describe data-flow pruning, a new data-flow sensitive fault-space pruning method that extends on def/use-pruning by also considering the instructions’ semantics when deriv- ing fault-equivalence sets. By tracking the information flow for each bit individually across the respective instructions and considering their fault-masking capability, data-flow pruning (DFP) has to plan fewer pilot injections as it derives larger fault-equivalence sets. Like def/use pruning, DFP is precise and complete and it can be used as a direct replace- ment/alternative in existing software-based fault-injection tools. Our prototypical implementation so far considers lo- cal fault equivalence for five types of instructions. In our experimental evaluation, this already reduces the number of necessary injections by up to 18 percent compared to def/use pruning.
AB - In the domain of safety-critical systems, fault injection cam- paigns on ISA-level have become a widespread approach to systematically assess the resilience of a system with respect to transient hardware faults. However, experimentally in- jecting all possible faults to achieve full fault-space coverage is infeasible in practice. Hence, pruning techniques, such as def/use pruning are commonly applied to reduce the cam- paign size by grouping injections that surely provoke the same erroneous behavior.We describe data-flow pruning, a new data-flow sensitive fault-space pruning method that extends on def/use-pruning by also considering the instructions’ semantics when deriv- ing fault-equivalence sets. By tracking the information flow for each bit individually across the respective instructions and considering their fault-masking capability, data-flow pruning (DFP) has to plan fewer pilot injections as it derives larger fault-equivalence sets. Like def/use pruning, DFP is precise and complete and it can be used as a direct replace- ment/alternative in existing software-based fault-injection tools. Our prototypical implementation so far considers lo- cal fault equivalence for five types of instructions. In our experimental evaluation, this already reduces the number of necessary injections by up to 18 percent compared to def/use pruning.
KW - bit flip
KW - fault injection
KW - fault-space pruning
KW - functional correctness
KW - reliability
KW - single event upset
UR - http://www.scopus.com/inward/record.url?scp=85109358101&partnerID=8YFLogxK
U2 - 10.1145/3461648.3463851
DO - 10.1145/3461648.3463851
M3 - Conference contribution
T3 - Proceedings of the ACM SIGPLAN Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES)
SP - 97
EP - 109
BT - LCTES 2021
A2 - Henkel, Jorg
A2 - Liu, Xu
T2 - 22nd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems
Y2 - 22 June 2021
ER -