A New SEC-DED Error Correction Code Subclass for Adjacent MBU Tolerance in Embedded Memory
Why this work is in the frame
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Bibliographic record
Abstract
The reliability concern associated with radiation-induced soft errors in embedded memories increases as semiconductor technology scales deep into the sub-40-nm regime. As the memory bit-cell area is reduced, single event upsets (SEUs) that would have once corrupted only a single bit-cell are now capable of upsetting multiple adjacent memory bit-cells per particle strike. While these error types are beyond the error handling capabilities of the commonly used single error correction double error detection (SEC-DED) error correction codes (ECCs) in embedded memories, the overhead associated with moving to more sophisticated double error correction (DEC) codes is considered to be too costly. To address this, designers have begun leveraging selective bit placement to design SEC-DED codes capable of double adjacent error correction (DAEC) or triple adjacent error detection (TAED). These codes can be implemented for the same check-bit overhead as the conventional SEC-DED codes; however, no codes have been developed that use both DAEC and TAED together. In this paper, a new ECC scheme is introduced that provides not only the basic SEC-DED coverage but also both DAEC and scalable adjacent error detection ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$x$</tex></formula> AED) with a reduction in miscorrection probability as well. Codes capable of up to 11-bit AED have been developed for both 16- and 32-bit standard memory word sizes, and a (39, 32) SEC-DED-DAEC-TAED code implementation that uses the same number of check-bits as a conventional 32-data-bit SEC-DED code is presented.
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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 | 0.001 | 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