Cost models for large file memory DRAMs with ECC and bad block marking
Why this work is in the frame
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Bibliographic record
Abstract
We present cost models appropriate for large file memory DRAMs that exploit error-correcting codes, redundant elements and bad block marking in order to reduce the average cost per working bit. Many different fault-tolerance methods have been considered previously for DRAMs but, because of the constraints of conventional commodity memory, only a few methods, such as redundant rows and columns, have entered wide-spread use. Our research on file memory breaks from past work by relaxing the requirements that random-access be fast and that shipped devices contain 100% of the nominal working bit capacity. We show that, under the relaxed requirements of file memory, the greater potential efficiencies of large ECC codewords and bad block marking may become cost-effective. These file memory techniques may thus be a way of accelerating the economic production of 256 Mbit and 1 Gbit DRAMs.
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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) | 0.000 | 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