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Record W1634867344 · doi:10.1109/isit.2015.7282693

Scaling Rules for the Energy of Decoder Circuits

2015· article· en· W1634867344 on OpenAlexaff
Christopher Blake, Frank R. Kschischang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectronic circuitDecoding methodsEnergy (signal processing)Block (permutation group theory)Computer scienceScalingVery-large-scale integrationUpper and lower boundsFunction (biology)Topology (electrical circuits)AlgorithmDiscrete mathematicsMathematicsCombinatoricsPhysicsGeometryStatistics

Abstract

fetched live from OpenAlex

A standard VLSI model is used to derive universal lower bounds on the energy of decoder circuits. In the circuit model used, the product of the circuit area and number of clock cycles, or the area-time complexity is proportional to the energy of computation. Lower bounds as a function of block length n are presented for three different circuit paradigms. Firstly, for circuits that compute in parallel, an Ω(n(logn)1/2) scaling rule is shown. Secondly, for circuits that compute serially, an Ω(nlogn) lower bound is presented. Thirdly, for a sequence of decoding circuits in which the number of output pins grows arbitrarily with block length, the energy is shown to grow as Ω(n(logn)1/5). In addition, it is shown that the energy complexity of almost all LDPC decoders that can get close to capacity and whose Tanner graphs are generated according to a uniform standard configuration model must take Ω(n2) area to implement directly.

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)0.002
metaresearch head score (Gemma)0.018
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.002

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.

Opus teacher head0.063
GPT teacher head0.296
Teacher spread0.233 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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