Energy-efficient decoding of low-density parity-check codes
Bibliographic record
Abstract
Low-density parity-check (LDPC) codes are a type of error correcting code that are frequently used in high-performance communications systems, due to their ability to approach the theoretical limits of error correction. However, their iterative soft-decision decoding algorithms suffer from high computational complexity, energy consumption, and auxiliary circuit implementation difficulties. It is of particular interest to develop energy-efficient LDPC decoders in order to decrease cost of operation, increase battery life in portable devices, lessen environmental impact, and increase the range of applications for these powerful codes.In this dissertation, we propose four new LDPC decoder designs with the primary goal of improving energy efficiency over previous designs. First, we present a bidirectional interleaver based on transmission gates, which reduces wiring complexity and associated parasitic energy losses. Second, we present an iterative decoder design based on pulse-width modulated min-sum (PWM-MS). We demonstrate that the pulse width message format reduces switching activity, computational complexity, and energy consumption compared to other recent LDPC decoder designs. Third, wepresent decoders based on differential binary (DB) algorithms. We also propose an improved differential binary (IDB) decoding algorithm, which greatly increases throughput and reduces energy consumption compared to recent decoders ofsimilar error correction capability. Finally, we present decoders based on gear-shift algorithms, which use multiple decoding rules to minimize energy consumption. We propose gear-shift pulse-width (GSP) and IDB with GSP (IGSP) algorithms, and demonstrate that they achieve superior energy efficiency without compromising error correction performance.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".