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Record W2128264388 · doi:10.1145/1278480.1278563

Using negative edge triggered ffs to reduce glitching power in FPGA circuits

2007· article· en· W2128264388 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

VenueProceedings - ACM IEEE Design Automation Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLookup tableField-programmable gate arrayComputer scienceElectronic circuitDissipationSignal edgeRouting (electronic design automation)Enhanced Data Rates for GSM EvolutionLogic gatePower (physics)Electronic engineeringMacrocell arrayElectrical engineeringLogic synthesisComputer hardwareEngineeringEmbedded systemAlgorithmLogic familyPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents an algorithm for reducing dynamic power dissipated by Field-Programmable Gate Array (FPGA) circuits. The algorithm uses a fast probability based model to estimate glitches on wires in a circuit and then inserts negative edge triggered FFs at outputs of Lookup Tables (LUTs) that produce glitches. A negative edge triggered FF maintains the logic value produced by the LUT in the previous cycle for the first half of the clock period, filtering glitches that occur at the output of the LUT. The power dissipation is lowered by reducing the number of transitions that propagate to the general routing network.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.075
GPT teacher head0.297
Teacher spread0.223 · 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