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
As memory systems scale, maintaining their Reliability Availability and Serviceability (RAS) is becoming more complex. To make matters worse, recent studies of DRAM failures in data centers and supercomputer environments have highlighted that large-granularity failures are common in DRAM chips. Furthermore, the move toward 3D-stacked memories can make the system vulnerable to newer failure modes, such as those occurring from faults in Through-Silicon Vias (TSVs). To architect future systems and to use emerging technology, system designers will need to employ strong error correction and repair techniques. Unfortunately, evaluating the relative effectiveness of these reliability mechanisms is often difficult and is traditionally done with analytical models, which are both error prone and time-consuming to develop. To this end, this article proposes F ault S im , a fast configurable memory-reliability simulation tool for 2D and 3D-stacked memory systems. FaultSim employs Monte Carlo simulations, which are driven by real-world failure statistics. We discuss the novel algorithms and data structures used in FaultSim to accelerate the evaluation of different resilience schemes. We implement BCH-1 (SECDED) and ChipKill codes using FaultSim and validate against an analytical model. FaultSim implements BCH-1 and ChipKill codes with a deviation of only 0.032% and 8.41% from the analytical model. FaultSim can simulate 1 million Monte Carlo trials (each for a period of 7 years) of BCH-1 and ChipKill codes in only 34 seconds and 33 seconds, respectively.
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