Encoder architecture with throughput over 10 Gbit/sec for quasi-cyclic LDPC codes
Bibliographic record
Abstract
This paper discusses the design of a high-speed encoder for low density parity check (LDPC) codes. To minimize hardware costs and memory requirements of such encoders, a class of high-performance quasi-cyclic LDPC codes which can be encoded in linear time has been proposed by designing the parity check matrix in a triangular plus dual-diagonal form. Based on the proposed codes, parallel architectures and pipelining technology have been used to increase the throughput of encoders. Moreover, collisions which occur when parallel processors contend for write access to the same memory module are avoided by exploiting an iterative encoding approach which involves repeated usage of the processors. The implementation results into field programmable gate array (FPGA) devices indicate that the encoder for the LDPC code with a block length of 2048 and a code rate of 0.5 attains a throughput of 12.8 Gbit/s using 352 exclusive-OR gates.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".