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Record W2098762322 · doi:10.1109/fpt.2005.1568543

Dynamic voltage scaling for commercial FPGAs

2006· article· en· W2098762322 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
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRobustness (evolution)Field-programmable gate arrayComputer scienceVoltageDynamic voltage scalingInverterScalingChipElectronic circuitMargin (machine learning)Electronic engineeringElectrical engineeringEmbedded systemEngineeringMathematics

Abstract

fetched live from OpenAlex

A methodology for supporting dynamic voltage scaling (DVS) on commercial FPGAs is described. A logic delay measurement circuit (LDMC) is used to determine the speed of an inverter chain for various operating conditions at run time. A desired LDMC value, intended to match the critical path of the operating circuit plus a safety margin, is then chosen; a closed loop control scheme is used to maintain the desired LDMC value as chip temperature changes, by automatically adjusting the voltage applied to the FPGA. We describe experiments using this technique on various circuits at different clock frequencies and temperatures to demonstrate its utility and robustness. Power savings between 4% and 54% for the V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">INT</sub> supply are observed

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score0.571

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Citations108
Published2006
Admission routes1
Has abstractyes

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