Bibliographic record
Abstract
DRAM systems continue to be plagued by the Row-Hammer (RH) security vulnerability. The threshold number of row activations (TRH) required to induce RH has reduced rapidly from 139K in 2014 to 4.8K in 2020, and TRH is expected to reduce further, making RH even more severe for future DRAM. Therefore, solutions for mitigating RH should be effective not only at current TRH but also at future TRH. In this paper, we investigate the mitigation of RH at ultra-low thresholds (500 and below). At such thresholds, state-of-the-art solutions, which rely on SRAM or CAM for tracking row activations, incur impractical storage overheads (340KB or more per rank at TRH of 500), making such solutions unappealing for commercial adoption. Alternative solutions, which store per-row metadata in the addressable DRAM space, incur significant slowdown (25% on average) due to extra memory accesses, even in the presence of metadata caches. Our goal is to develop scalable RH mitigation while incurring low SRAM and performance overheads.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.104 |
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 source (direct Gemma or distilled Codex), 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".