A Memory-Based Architecture for FPGA Implementations of Low-Density Parity-Check Convolutional Decoders
Why this work is in the frame
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Bibliographic record
Abstract
Low-density parity-check convolutional codes complement their popular block-oriented counterparts and may be more suitable in certain communication applications. These include streaming voice and video and packet switching networks. In this paper we introduce these codes and propose a memory-based decoder architecture that is well suited for implementation on field-programmable gate arrays. We present an overview of the architecture and demonstrate its efficiency over register-based architectures. We then discuss a realization of this architecture that can trade performance for throughput and can achieve up to 120 Mb/s of information throughput and a BER as low as 2 /spl times/ 10/sup -6/ at an Eb/Nq of 3 dB on an Altera Stratix FPGA. For a first-generation implementation this compares favorable with current block-oriented decoder implementations.
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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 | 0.000 | 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 | 0.001 | 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