Reduced-memory high-throughput fast-SSC polar code decoder architecture
Bibliographic record
Abstract
Polar codes have been selected for use within 5G networks, and are being considered for data and control channel for additional 5G scenarios, like the next generation ultra reliable low latency channel. As a result, efficient fast polar code decoder implementations are essential. In this work, we present a new fast simplified successive cancellation (Fast-SSC) decoder architecture. Our proposed solution is able to reduce the memory requirements and has an improved throughput with respect to state of the art Fast-SSC decoders. We achieve these objectives through a more efficient memory utilization than that of Fast-SSC, which also enables to execute multiple instructions in a single clock cycle. Our work shows that, compared to the state of the art, memory requirements are reduced by 22.2%; at the same time, a throughput improvement of 11.6% is achieved with (1024, 512) polar codes. Comparing equal throughputs, the memory requirements are reduced by up to 60.4%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".