3.2-Gb/s 1024-b rate-1/2 LDPC decoder chip using a flooding-type update-schedule algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a high-speed low-density parity-check (LDPC) decoder chip using a new decoding algorithm, called a flooding-type update-schedule algorithm. Since node computations are performed using partially updated messages in the proposed algorithm, because of the good similarity among time-consecutive messages, data-transmission bottleneck between nodes for node computation is greatly reduced. Moreover, longer wires between nodes are appropriately divided into several subwires by inserting flip-flops so that system clock frequency for the LDPC decoding scheme can be much increased while maintaining the same BER as a conventional algorithm using fully updated messages. In fact, a throughput of 3.2Gb/s in a 1024-b LDPC decoder chip under 90nm CMOS technology is attained with the sufficient BER.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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