High-throughput LDPC decoding using the RHS algorithm
Bibliographic record
Abstract
The relaxed half-stochastic (RHS) algorithm is a recently proposed binary message-passing decoding algorithm for low-density parity check codes that can reach the same error rate performance as belief propagation algorithms that exchange LLR messages. Because of its low-complexity interleaver, the RHS algorithm makes it possible to achieve a fully-parallel implementation that can converge to a codeword in only a few clock cycles on average, enabling high throughput and power efficiency. To demonstrate the practicality of the RHS algorithm, we implement a decoder for the popular IEEE 802.3an 10GBASE-T standard. The paper presents details of the hardware implementation, as well as post-layout results for an ASIC implementation in 65nm CMOS technology, which indicate that the decoder can operate at 448 MHz and occupies an area of 4.41 mm2. The results obtained from bit-accurate software simulations show that the decoder meets the latency requirement prescribed by the standard and provides an average throughput of 160 Gbps.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".