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Record W2129984271 · doi:10.1145/1013235.1013325

Efficient adaptive voltage scaling system through on-chip critical path emulation

2004· article· en· W2129984271 on OpenAlex
M. Elgebaly, Manoj Sachdev

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 Waterloo
FundersQualcomm
KeywordsParasitic extractionEmulationComputer scienceScalingInterconnectionCritical path methodVoltageSystem on a chipElectronic engineeringVery-large-scale integrationPath (computing)ChipEmbedded systemEngineeringElectrical engineeringMathematicsComputer networkTelecommunications

Abstract

fetched live from OpenAlex

Conventional voltage scaling techniques rely on the characterization and monitoring of a unique critical path. However, the uniqueness of the critical path is a difficult requirement to establish in modern VLSI technologies due to the growing impact of process variations and interconnect parasitics on delay. This paper presents an on-chip critical path emulator architecture which tracks the changing critical path. The ability to emulate the actual critical path recovers most of the large margin added by conventional systems to guarantee a robust operation at all conditions. Due to the reduced margin, the proposed architecture is up to 45% and 21% more energy efficient compared to conventional open-loop and closed-loop voltage scaling systems respectively.

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 score0.630
Threshold uncertainty score0.829

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)0.0000.001

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 head0.015
GPT teacher head0.228
Teacher spread0.212 · 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

Citations30
Published2004
Admission routes1
Has abstractyes

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