Power efficient error correction coding for on‐chip interconnection links
Bibliographic record
Abstract
A configurable self‐calibrated power efficient five‐bit error correction code is proposed to correct both single bit random and burst errors up to five bits; providing 100% error correction probability with crosstalk avoidance. It can also correct higher‐order error up to 9 bits with an error correction probability tolerance of 73% for on‐chip interconnection links. Single error correction and double error detection with extended Hamming code (22,16) is utilised along with standard triplication error correction methods in the proposed code. Self‐calibration algorithm and data stream rerouting block are integrated into the error correction code to achieve power efficiency. Reliability, link power consumption, and link swing voltage are estimated using an analytical model used in a network‐on‐chip. Area, power, and delay of the codec are obtained using Synopsys tools utilising UMC 90 nm technology. The proposed method provides 32–73% power saving and 22.3–60.6% delay reduction with negligible area overhead compared with the state‐of‐the‐art works. Estimated results prove that it provides a 40.5–50% reduction in link swing voltage and link power consumption compared with the state‐of‐the‐art works. The proposed code is more appropriate for on‐chip interconnect links where it provides high reliability and low swing voltage with high error correction capability compared with existing codes.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".