Asynchronous Stochastic Decoding of Low-Density Parity-Check Codes
Bibliographic record
Abstract
This paper presents an asynchronous scheduling algorithm for high-throughput stochastic low-density parity-check (LDPC) decoders. Stochastic computation provides ultra-low-complexity hardware and can be implemented using binary or multiple-valued logic gates. Using asynchronous control, it also eliminates a global clock signal and therefore eases the worst-case timing restrictions. A timing model of asynchronous-computation behaviours under a 90nm CMOS technology is used to demonstrate that the proposed algorithm with an optimized computation delay properly decodes a regular (1024, 512) LDPC code without the "lock-up" problem that potentially stops decoding before convergence and hence causes loss in coding gain. Based on our models, the proposed scheme achieves up to 7.37x improvement in decoding throughput with comparable BER performance in comparison with performance results of a conventional synchronous stochastic decoder.
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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 | 0.002 |
| 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) | 0.001 | 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".