FPGA Implementation of LDPC Decoders Based on Joint Row-column Decoding Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a joint row-column decoding algorithm for the decoding of low-density parity-check (LDPC) codes. Simulation indicates that the proposed algorithm improves the performance in both the waterfall region and the error floor region. By combining row processing with column processing, the joint row-column decoding algorithm reduces the storage requirements of extrinsic messages and avoids memory conflicts and routing congestion during the exchanges of extrinsic messages. Implementation results into field programmable gate array (FPGA) devices indicate that the proposed algorithm reduces the hardware costs by 30% and increases the decoding speed by a factor of four. A 40-parallel decoder attains a throughput of 2 Gbits/sec by using up to 20 % of the generic logic resources in a Xilinx XC4LX160 device
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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