A multiword based high speed ECC scheme for low-voltage embedded SRAMS
Bibliographic record
Abstract
This paper presents a multiword based error correction code (MECC) scheme to mitigate SEUs in low-voltage SRAMs. MECC combines four 32 bit data words to form a composite 128 bit ECC word and uses optimized transmission-gate XOR logic, thus significantly reducing check-bit overhead and error correction time, respectively. Use of composite word warrants a unique write operation where MECC updates checkbits by simultaneously writing one data word and reading the other three data words. Two composite words are interleaved in a row to tackle multi-bit SEU. In addition, the supply voltage of the SRAM is reduced to save leakage and active power. A 64kb SRAM with MECC implemented in 90nm CMOS technology consumes 154 muW leakage power and 375 muW active power at 0.6 V and 100 MHz, showing improved area and speed-power efficiency than conventional single-word ECC and existing multiword ECC schemes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".