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Record W2003652851 · doi:10.1109/isqed.2005.98

Power-Delay Metrics Revisited for 90nm CMOS Technology

2005· article· en· W2003652851 on OpenAlex
Dipanjan Sengupta, R. Saleh

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCMOSComputer sciencePower (physics)Electronic engineeringLogic gateElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Recently, designers have been using the energy-delay product as a metric of goodness for CMOS designs due to certain perceived shortcomings of the more traditional power-delay product. As the industry moves to 90 nm technology, with higher leakage currents, it is an appropriate time to revisit existing design metrics. We provide a more general view of power and delay metrics for design optimization and then illustrate how these metrics can be used. To do so, a re-evaluation of the metrics, based on past and future trends, is carried out and a set of new metrics is proposed. Interestingly, the dominance of leakage power at 90 nm technology and beyond tends to reduce the feasible operating region. We also establish a fundamental relationship between the optimal operating points and the generalized design metrics. Moreover, our initial findings indicate that some designs may need to leak more than expected to achieve certain design targets, running somewhat counter to conventional wisdom.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations24
Published2005
Admission routes2
Has abstractyes

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