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Record W2963021833 · doi:10.1109/tit.2017.2673805

On the VLSI Energy Complexity of LDPC Decoder Circuits

2017· article· en· W2963021833 on OpenAlexafffund
Christopher Blake, Frank R. Kschischang

Bibliographic record

VenueIEEE Transactions on Information Theory · 2017
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVery-large-scale integrationLow-density parity-check codeDecoding methodsComputer scienceElectronic circuitEnergy (signal processing)Forward error correctionElectronic engineeringAlgorithmMathematicsElectrical engineeringEngineeringEmbedded system

Abstract

fetched live from OpenAlex

Sequences of randomly generated bipartite configurations are analyzed; under mild conditions almost surely such configurations have minimum bisection width proportional to the number of vertices. This implies an almost sure Ω(n2/dmax2) scaling rule for the energy of directlyimplemented low-density parity-check (LDPC) decoder circuits for codes of block length n and maximum node degree dmax. It also implies an Ω(n3/2/dmax) lower bound for serialized LDPC decoders. It is also shown that all (as opposed to almost all) capacity-approaching, directly-implemented non-split-node LDPC decoding circuits, have energy, per iteration, that scales as Ω(χ2ln3χ), where χ = (1 - R/C)-1is the reciprocal gap to capacity, R is code rate, and C is channel capacity.

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)0.001
metaresearch head score (Gemma)0.011
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.036
GPT teacher head0.265
Teacher spread0.228 · 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

Citations14
Published2017
Admission routes2
Has abstractyes

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