Power Reduction Techniques for LDPC Decoders
Bibliographic record
Abstract
This paper investigates VLSI architectures for low-density parity-check (LDPC) decoders amenable to low- voltage and low-power operation. First, a highly-parallel decoder architecture with low routing overhead is described. Second, we propose an efficient method to detect early convergence of the iterative decoder and terminate the computations, thereby reducing dynamic power. We report on a bit-serial fully-parallel LDPC decoder fabricated in a 0.13-$\mu{\hbox{m}}$CMOS process and show how the above techniques affect the power consumption. With early termination, the prototype is capable of decoding with 10.4 pJ/bit/iteration, while performing within 3 dB of the Shannon limit at a BER of 10$^{-5}$and with 3.3 Gb/s total throughput. If operated from a 0.6 V supply, the energy consumption can be further reduced to 2.7 pJ/bit/iteration while maintaining a total throughput of 648 Mb/s, due to the highly-parallel architecture. To demonstrate the applicability of the proposed architecture for longer codes, we also report on a bit-serial fully-parallel decoder for the (2048, 1723) LDPC code in 10GBase-T standard synthesized with a 90-nm CMOS library.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".