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Record W2153653002 · doi:10.1109/iscas.2006.1693418

Gate-level dual-threshold static power optimization methodology (GDSPOM) for designing high-speed low-power SOC applications using 90nm MTCMOS technology

2006· article· en· W2153653002 on OpenAlexaff
B. Chung, J.B. Kuo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsSierra Wireless (Canada)Simon Fraser University
Fundersnot available
KeywordsComputer sciencePower optimizationThreshold voltagePower consumptionPower (physics)Electronic engineeringMultiplier (economics)Low-power electronicsDual (grammatical number)CMOSLogic gateVoltageElectrical engineeringTransistorEngineering

Abstract

fetched live from OpenAlex

This paper reports a novel gate-level dual-threshold static power optimization methodology (GDSPOM), which is based on the static timing analysis technique, for designing high-speed low-power SOC applications using 90nm MTCMOS technology. Based on this optimization technique, using two cell libraries with different threshold voltages, a 16-bit multiplier using the dual-threshold cells meeting the speed requirement has been designed to have a 50% less power consumption as compared to the all low-threshold one

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.043
GPT teacher head0.267
Teacher spread0.225 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2006
Admission routes1
Has abstractyes

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