Intermittent Hardware Errors Recovery: Modeling and Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The frequency of hardware errors is increasing due to shrinking feature sizes, higher levels of integration, and increasing design complexity. Intermittent errors are those that occur non-deterministically at the same location. It has been shown that intermittent hardware errors contribute to about 39% of the total hardware failures. Intermittent faults have characteristics that are different than transient and permanent errors, which makes it challenging to devise efficient recovery techniques for them. In this paper, we evaluate the impact of different intermittent error recovery scenarios on the processor performance. To achieve this, we model a system that consists of a fault-tolerant multicore processor subject to intermittent faults. Our fault models are based on insights from related work at the physical level. We find that the frequency of the intermittent error and the relative importance of the error location play an important role in choosing the recovery action that maximizes the processor's performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it