A Soft-Error Tolerant Content-Addressable Memory (CAM) Using An Error-Correcting-Match Scheme
Bibliographic record
Abstract
Modern integrated circuits require careful attention to the soft-error rate (SER) resulting from bit upsets, which are normally caused by alpha particle or neutron hits. These events, also referred to as single-event upsets (SEUs), will become more problematic in future technologies. This paper presents a binary content-addressable memory (CAM) design with high immunity to SEUs. Conventionally, error-correcting codes (ECC) have been used in SRAMs to address this issue, but these techniques are not immediately applicable to CAMs because they depend on processing the full contents of the memory word outside the array, which is not possible in a normal CAM access. The proposed design consists of a new matching technique that uses coding to increase the Hamming distance between words, in conjunction with a modified matchline sensing scheme. The result is a CAM design that reduces the SER with no increase in delay or power dissipation, and with only a 12% increase in area
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".