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Record W2169184517 · doi:10.1109/ccece.1996.548128

A novel technique for pipelined scheduling and allocation of data-flow graphs based on genetic algorithms

2002· article· en· W2169184517 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceCrossoverScheduling (production processes)AlgorithmBenchmark (surveying)Genetic algorithmData flow diagramData-flow analysisParallel computingMathematical optimizationArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Genetic algorithms are exploited and applied to the development of a novel technique for the high-level synthesis of digital signal processing algorithms. The proposed technique performs the scheduling and allocation of functional units for the synthesis of both pipelined and non-pipelined data-paths. The salient feature of this technique is that it employs the order crossover operator in combination with a schedule building heuristic to overcome the well known encoding problem encountered when applying genetic algorithms to scheduling problems. The technique is demonstrated through its application to a benchmark fifth-order elliptic wave-digital filter. The results show that the technique produces the optimum schedules for non-pipelined data-paths, but requires further refinements to match the best existing schedules for pipelined data-paths.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.970
Threshold uncertainty score0.274

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 head0.089
GPT teacher head0.302
Teacher spread0.212 · 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

Quick stats

Citations3
Published2002
Admission routes1
Has abstractyes

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