Low-Latency Burst Error Detection and Correction in Decision-Feedback Equalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article describes low latency, zero overhead DFE burst error correction technique. Without any encoder or decoder latency, the proposed technique makes use of the existing pre-cursor ISI to detect and correct errors on a burst of data. The implemented proof-of-concept 2-tap DFE prototype in 65nm CMOS operates at 16 Gb/s and compensates 32 dB loss consuming 58 mW only. With an additional 18 mW, the receiver enables error correction capability that translates to 2-to-6 dB SNR gain depending on the pre-cursor magnitude. Experimental results demonstrate that for lossy channels where pre-cursor is 60% or higher of main, this error correction outperforms RS(528, 514) without any overhead and with much lower latency and power consumption.
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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