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Record W2097670457 · doi:10.1109/newcas.2004.1359023

Automatic synthesis from high level ASM to VHDL: a case study

2004· article· en· W2097670457 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

VenueThe 2nd Annual IEEE Northeast Workshop on Circuits and Systems, 2004. NEWCAS 2004. · 2004
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComputer scienceHigh-level synthesisVHDLProgramming languageComputer architectureEmbedded systemField-programmable gate array

Abstract

fetched live from OpenAlex

Reconfigurable systems now offer tens of millions equivalent gates, allowing highly parallel processing impossible to reach with general purpose processors. Nevertheless, designing a circuit is intrinsically more complicated than a software approach because space and time concepts must be considered together. Current HDLs, which use a low level description, are only accessible to highly qualified hardware designers and require much more time than a pure software solution. This paper demonstrates how the use of higher level HDL allows people with a software background to design complex architectures in a simple and an efficient way. Five sort algorithms have been designed and tested in a few days using our intermediate level HDL. Results show that the design time, circuit space and global performances are at least one order of magnitude better than a processor approach for a NoC and could easily go to three orders of magnitude.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.291
Teacher spread0.229 · 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