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Record W2167711864 · doi:10.1109/fpt.2003.1275744

Product-term based synthesizable embedded programmable logic cores

2004· article· en· W2167711864 on OpenAlexaff
Andy Yan, Steven J. E. Wilton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProgrammable logic arrayComputer scienceProgrammable logic deviceProgrammable Array LogicLookup tableComputer architectureSimple programmable logic deviceLogic synthesisComplex programmable logic deviceLogic familyBenchmark (surveying)Table (database)Logic optimizationLogic gateEmbedded systemAlgorithmProgramming language

Abstract

fetched live from OpenAlex

As integrated circuits become increasingly complex, the ability to make post-fabrication changes will become more important and attractive. This ability can be realized using programmable logic cores. Currently, such cores are available from vendors in the form of a "hard" layout. Previous work has suggested an alternative approach: vendors supply a synthesizable version of their programmable logic core and the integrated circuit designer synthesizes the programmable logic fabric using standard cells. This paper presents a new family of architectures for these synthesizable cores; unlike previous architectures which were based on lookup-tables, the new family of architectures is based on a collection of product-term arrays. Compared to lookup-table based architectures, the new architectures result in density improvements of 35% and speed improvements of 72% on standard benchmark circuits.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.263
Teacher spread0.239 · 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 designBench or experimental
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
Published2004
Admission routes1
Has abstractyes

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