Characterization of stack behavior under soft errors

Ma, Junchi; Wang, Yun

doi:10.23919/date.2017.7927234

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…If the return address is affected, all cases cause crashes without any exceptions in the experiments. e result is aligned with our prior approach, which tried to inject faults to ESP or EBP [27]. e prior approach showed that if a proper return address was not loaded it tended to incur crash.…”

Section: (I)supporting

confidence: 72%

Characterization of Program Behavior under Faulty Instruction Encoding

Duan

Tang

2022

Scientific Programming

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As process technology scales, electronic devices become more susceptible to soft errors. Soft errors can lead to silent data corruptions (SDCs), seriously compromising the reliability of a system. Researchers have explored error resilient encodings, which leverage crash patterns to detect SDCs. Despite its importance, much still remains to be determined regarding how errors propagate to cause SDCs or crashes. Understanding error propagation patterns could lead to more efficient implementation of error detection. An experimental study of program behavior in the presence of faulty instruction encoding under the IA-32 architecture is described in this study. Extensive fault injection experiments including over 70,000 faults were conducted, targeting all fields of instruction encoding. The analysis of the obtained data shows the following: (1) If the alignment of an instruction sequence is not preserved after injection, it causes crashes in a high probability (93.2%). (2) The SDC rate of an alignment-preserved category is close to that of a typical data injection. The SDC-prone fields include the opcode field, reg field, and immediate field. (3) Several crash patterns, such as violation of calling conventions, are revealed to extend the detection methods. These findings help us identify the vulnerable parts of instruction encoding, which need to be protected against soft errors. By applying the implications provided by the findings, we discuss feasible modifications, including swapping reg encodings, to reduce SDC rate, thus increasing the resilience of instruction set to soft errors.

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Section: (I)supporting

confidence: 72%

Characterization of Program Behavior under Faulty Instruction Encoding

Duan

Tang

2022

Scientific Programming

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“…For example, the probability that the result type is SDC after injecting a fault to EAX is higher than that of ESP, indicating that EAX has a higher SDC vulnerability than ESP. The research work in [14] demonstrates that 80% fault injections on ESP and EBP lead to crashes rather than SDCs, owning to the fact that ESP is used as a stack pointer and EBP is used as a stack frame base pointer. This means that both EBP and ESP have a lower SDC vulnerability.…”

Section: B Register-related Featuresmentioning

confidence: 99%

Identify Silent Data Corruption Vulnerable Instructions Using SVM

Yang¹,

Wang²

2019

IEEE Access

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Silent data corruption (SDC) is the most insidious and harmful result type of soft error. Identify program vulnerable instructions (PVIns) that are likely to cause SDCs is extremely significant on selective software-based protection techniques. However, current identification techniques require tremendous fault injections or have non-negligible differences in performance among different programs as well as different program inputs. This paper proposes PVInsiden to reduce the cost of fault injection and improve the adaptability for programs and program inputs. Machine learning is used to learn a classifier which predicts whether an instruction is a PVIns. Partial fault injection is applied to generate a training dataset, reducing the cost of fault injection. The feature engineering, including selecting features and transforming the selected features into quantifiable representations is explored. Furthermore, the framework of learning the classifier is given. The experimental results show that PVInsiden only uses 35% fault injections to identify 85% PVIns with 80% precision, reducing the cost of fault injection efficiently. PVInsiden also shows high performance of precision, recall, and f0.5-score for different programs as well as different program inputs.

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“…In our previous work [20], a series of fault injections on ESP and EBP are conducted. It is observed that error in ESP leads to benign or SDC only if ESP points to a return address when executing RET.…”

Section: Propagation Model: Enhanced Dynamic Dependence Graph (Eddg)mentioning

confidence: 99%

A Methodology to Assess Output Vulnerability Factors for Detecting Silent Data Corruption

Duan

Tang

2019

IEEE Access

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As process technology scales, electronic devices become more susceptible to soft error induced by radiation. Silent data corruption (SDC) is considered the most severe outcome incurred by soft error. The effects of faulty variables on producing SDC vary widely. Without a profiling of vulnerability of variables, the derived detectors often incur low SDC detection rate or unacceptable overhead. To assess the vulnerability of variables to SDC, this paper proposes a metric called Output Vulnerability Factor (OVF). The metric is used to rank the variable's priority in the detector derivation process in order to selectively protect the most SDC-prone variable in the program. The calculation of OVF is based on enhanced Dynamic Dependence Graph (eDDG), a proposed instruction-level error propagation model. We filter out the edges representing the identified crash propagation path and perform a backward traversal of the eDDG to obtain SDC propagation path. Further, error masking probability is estimated for the edges refer to value comparison and logistic operation. Fault injections show that our approach achieves an SDC detection rate of 65.0% with the top 10% high OVF variables monitored. Compared with previous methods, the SDC detection rate increases by 12-21%.

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Characterization of stack behavior under soft errors

Cited by 7 publications

References 13 publications

Characterization of Program Behavior under Faulty Instruction Encoding

Characterization of Program Behavior under Faulty Instruction Encoding

Identify Silent Data Corruption Vulnerable Instructions Using SVM

A Methodology to Assess Output Vulnerability Factors for Detecting Silent Data Corruption

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