Using Input-to-Output Masking for System-level Vulnerability estimation in high-performance processors
Bibliographic record
Abstract
In this paper, we enhance previously suggested vulnerability estimation techniques by presenting a detailed modeling technique based on Input-to-Output Masking (IOM). Moreover we use our model to compute the System-level Vulnerability Factor (SVF) for data-path components in a high-performance processor. As we show, recent suggested estimation techniques overlook the issue of error masking, mainly focusing on time periods in which an error could potentially propagate in the system. In this work we show that this is incomplete as it ignores the masking impact. Our results show that including the IOM factor can significantly affect the system-level vulnerability for data-path components. As a case study, we analyze the IOM factor for CPUs with different configurations. Our results show that the average variation of the IOM factor is less than 5%. Meantime, the IOM factor varies between 24% to 76% for the applications studied here. Accordingly we find the IOM factor to be less configuration dependent and mainly workload dependent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".