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Record W2156063738 · doi:10.1109/tcsii.2007.905328

Block-Interlaced LDPC Decoders With Reduced Interconnect Complexity

2008· article· en· W2156063738 on OpenAlexaff
Ahmad Darabiha, Anthony Chan Carusone, Frank R. Kschischang

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2008
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterlacingLow-density parity-check codeComputer scienceParallel computingThroughputBlock (permutation group theory)Overhead (engineering)Code (set theory)Decoding methodsComputer hardwareAlgorithmMathematicsWirelessTelecommunications

Abstract

fetched live from OpenAlex

Two design techniques are proposed for high-throughput low-density parity-check (LDPC) decoders. A broadcasting technique mitigates routing congestion by reducing the total global wirelength. An interlacing technique increases the decoder throughput by processing two consecutive frames simultaneously. The brief discusses how these techniques can be used for both fully parallel and partially parallel LDPC decoders. For fully parallel decoders with code lengths in the range of a few thousand bits, the half-broadcasting technique reduces the total global wirelength by about 26% without any hardware overhead. The block interlacing scheme is applied to the design of two fully parallel decoders, increasing the throughput by 60% and 71% at the cost of 5.5% and 9.5% gate count overhead, respectively.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.047
GPT teacher head0.253
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations64
Published2008
Admission routes1
Has abstractyes

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