MétaCan
Menu
Back to cohort
Record W2072367650 · doi:10.1109/fpl.2014.6927498

Effective FPGA debug for high-level synthesis generated circuits

2014· article· en· W2072367650 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
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDebuggingField-programmable gate arrayComputer scienceHigh-level synthesisEmbedded systemSoftwareBackground debug mode interfaceComputer architectureSource codeTime to marketCode (set theory)Operating systemProgramming languageSet (abstract data type)

Abstract

fetched live from OpenAlex

High-level synthesis (HLS) promises to increase designer productivity in the face of steadily increasing FPGA sizes, and broaden the market of use, allowing software designers to reap the benefits of hardware implementation. One roadblock to HLS adoption is the lack of a debugging infrastructure. To debug, designers can run their source code on a processor; however, this does not capture interactions with other system components. The alternative is to debug using the RTL, which is beyond the expertise of software designers, and impractical for hardware designers as the RTL may not resemble the original source code.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.029
GPT teacher head0.252
Teacher spread0.224 · 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

Citations49
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicEmbedded Systems Design TechniquesFrench-language works237,207