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Record W2082703745 · doi:10.1109/sips.2014.6986088

FPGA implementation of a clockless stochastic LDPC decoder

2014· article· en· W2082703745 on OpenAlexaff
Chris Ceroici, Vincent Gaudet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStratixField-programmable gate arrayComputer scienceLow-density parity-check codeDecoding methodsThroughputParallel computingGate arrayAdderCombinational logicMultiplication (music)Computer hardwareArithmeticLogic gateAlgorithmMathematicsLatency (audio)

Abstract

fetched live from OpenAlex

This paper demonstrates a clockless stochastic low-density parity-check (LDPC) decoder implemented on a Field-Programmable Gate Array (FPGA). Stochastic computing reduces the wiring complexity necessary for decoding by replacing operations such as multiplication and division with simple logic gates and serial processing. Clockless decoding increases the throughput of the decoder by eliminating the requirement for node signals to be synchronized after each decoding cycle. The design is implemented on an ALTERA Stratix IV EP4SGX230 FPGA and the frame error rate (FER), throughput, and power performance are presented for (96,48) and (204,102) LDPC decoders.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.305
Teacher spread0.292 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

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

Citations5
Published2014
Admission routes1
Has abstractyes

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