Energy-Efficient Hardware Architectures for Fast Polar Decoders
Bibliographic record
Abstract
Interest in polar codes has increased significantly upon their selection as a coding scheme for the 5th generation wireless communication standard (5G). While the research on polar code decoders mostly targets improved throughput, few implementations address energy consumption, which is critical for platforms that prioritize energy efficiency, such as massive machine-type communications (mMTC). In this work, we first propose a novel Fast-SSC decoder architecture that has novel architectural optimizations to reduce area, power, and energy consumption. Then, we extend our work to an energy-efficient implementation of the fast SC-Flip (SCF) decoder. We show that sorting a limited number of indices for extra decoding attempts is sufficient to practically match the performance of SCF, which enables employing a low-complexity sorter architecture. To our knowledge, the proposed SCF architecture is the first hardware realization of fast SCF decoding. Synthesis results targeting TSMC 65nm CMOS technology show that the proposed Fast-SSC decoder architecture is 18% more energy-efficient, has 14% less area and 30% less power consumption compared to state-of-the-art decoders in the literature. Compared to the state-of-the-art available SC-List (SCL) decoders that have equivalent error-correction performance, proposed Fast-SCF decoder is 29% faster while being 2.7× more energy-efficient and 51% more area-efficient.
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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) | 0.004 | 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".