Memory-Efficient Polar Decoders
Bibliographic record
Abstract
Polar codes have gained a great amount of attention in the past few years, since they can provably achieve the capacity of a symmetric channel with a low-complexity encoding and decoding algorithm. As a result, polar codes have been selected as a coding scheme in the 5th generation wireless communication standard. Among different decoding schemes, successive-cancellation (SC) and SC list decoding yield good trade-off between error-correction performance and hardware implementation cost. However, both families of algorithms have large memory requirements. In this paper, we propose a set of novel techniques that aim at reducing the high-memory cost of SC-based decoders. These techniques are orthogonal to the specific decoder architecture considered, and can be applied on top of existing memory reduction techniques. We have designed and implemented different polar decoders on FPGA and also synthesized them in 65 nm TSMC CMOS technology to verify the effectiveness of the proposed memory reduction techniques. The benchmark decoders yield comparable or lower area occupation than the state of the art: the results show that the proposed methods can save up to 46% memory area occupation and 42% total area occupation compared with benchmark SC-based decoders.
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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 | 0.001 | 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) | 0.003 | 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".