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Record W1995613452 · doi:10.1049/iet-cdt:20070120

SC Build: a computer-aided design tool for design space exploration of embedded central processing unit cores for field-programmable gate arrays

2008· article· en· W1995613452 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

VenueIET Computers & Digital Techniques · 2008
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDesign space explorationComputer scienceGate arrayField-programmable gate arrayField (mathematics)Genetic algorithmComputer Aided DesignDesign toolComputer architectureSpace explorationCore (optical fiber)Space (punctuation)Embedded systemMulti-core processorComputer engineeringComputer hardwareParallel computingEngineeringAerospace engineeringOperating system

Abstract

fetched live from OpenAlex

A genetic algorithm-based design space exploration technique using parameterised cores is examined. A computer-aided design tool called SCBuild was developed which is capable of applying a genetic algorithm to a core's parameters, and generating hardware description language models of core variants. The tool can also compute estimates of a variant's area and critical path delay on a field-programmable gate array. Using this tool, several experiments were conducted using a soft-core processor with a large design space. It was concluded from these experiments that using a genetic algorithm to explore the design space of a parameterised core can help a designer make intelligent decisions regarding the assignment of values to the parameters of an embedded hardware platform.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.259
Teacher spread0.210 · 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