Asynchronous Stochastic Decoding of LDPC Codes: Algorithm and Simulation Model
Bibliographic record
Abstract
Stochastic decoding provides ultra-low-complexity hardware for high-throughput parallel low-density parity-check (LDPC) decoders. Asynchronous stochastic decoding was proposed to demonstrate the possibility of low power dissipation and high throughput in stochastic decoders, but decoding might stop before convergence due to “lock-up”, causing error floors that also occur in synchronous stochastic decoding. In this paper, we introduce a wire-delay dependent (WDD) scheduling algorithm for asynchronous stochastic decoding in order to reduce the error floors. Instead of assigning the same delay to all computation nodes in the previous work, different computation delay is assigned to each computation node depending on its wire length. The variation of update timing increases switching activities to decrease the possibility of the “lock-up”, lowering the error floors. In addition, the WDD scheduling algorithm is simplified for the hardware implementation in order to eliminate time-averaging and multiplication functions used in the original WDD scheduling algorithm. BER performance using a regular (1024, 512) (3,6) LDPC code is simulated based on our timing model that has computation and wire delay estimated under ASPLA 90nm CMOS technology. It is demonstrated that the proposed asynchronous decoder achieves a 6.4-9.8× smaller latency than that of the synchronous decoder with a 0.25-0.3 dB coding gain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".