Adaptive Reliability Chipkill Correct (ARCC)
Why this work is in the frame
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Bibliographic record
Abstract
Chipkill correct is an advanced type of error correction in memory that is popular among servers. Large field studies of memories have shown that chipkill correct reduces uncorrectable error rate by 4X [11] to 36X [12] compared to SECDED. Currently, there is a strong trade-off between power and reliability among different chipkill correct solutions. For example, commercially available chipkill correct solutions that can detect up to two failed devices and correct one (eg. SCCDCD) or two (eg. Double Chip Sparing) failed devices require accessing 36 DRAM devices per memory request. However, a weaker single chipkill correct single chipkill detect solution only requires accessing 18 devices per memory request and, therefore consumes much lower memory power. In this paper, we present Adaptive Reliability Chipkill Correct (ARCC) - an optimization to be applied to existing chipkill correct solutions to allow them to incur the low power consumption of a lower strength chipkill correct solution while maintaining similar reliability as that of a stronger chipkill correct solution. ARCC is based on the observation that, on average, only a tiny fraction of memory experiences any type of faults during the typical operational lifespan of a server. As such, it proposes relaxing the strength of chipkill correct in the beginning and then adaptively increasing the strength as needed on a page by page basis in order to reap the benefit of lower power consumption during the majority of the lifetime of a memory system. Our evaluation shows that ARCC reduces the power consumption of memory by 36%, on average, when applied to commercial SCCDCD, while keeping the storage overhead the same and maintaining similar reliability.
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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) | 0.001 | 0.001 |
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