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Record W2021543189 · doi:10.1145/775832.775838

Reshaping EDA for power

2003· article· en· W2021543189 on OpenAlexaff
Jam Rabaey, Dennis Sylvester, David Blaauw, Kerry Bernstein, Jerry Frenkil, Mark Horowitz, Wolfgang Nebel, Takayasu Sakurai, Andrew T. Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsCMOSNMOS logicStandby powerPower optimizationComputer scienceDesign flowIntegrated circuit designScalingElectronic design automationPower (physics)Frequency scalingCADElectronic engineeringElectrical engineeringReliability engineeringEmbedded systemPower consumptionEngineeringVoltageTransistor

Abstract

fetched live from OpenAlex

Today's rising power densities have been widely cited as the foremost challenge to continued CMOS scaling. In fact, the current power crisis is reminiscent of the final days previous technologies, such as the once popular bipolar and NMOS technologies and even vacuum tubes. How CMOS technology will respond to the current power challenge to extend CMOS scaling to sub-90nm technology is an important question for designer and CAD tool developers alike. With aggressive scaling a number of new challenges have arisen, such as leakage control, heat removal and power supply distribution, that need to be addressed using new design techniques in conjunction with new CAD solutions.This panel brings together experts in circuit design and CAD tool development to discuss the current status of low-power design and provide opinions on what new EDA capabilities are most important in the power-constrained design era. For instance, how will power be distributed in a robust fashion in sub-90nm ICs, and what are the critical EDA analysis and optimization capabilities? What are the best techniques for leakage reduction, not only in standby modes, but also in the active mode? And how far will voltage scaling take us in attacking the dynamic power consumption issue? What will a power-centric design flow look like and how will it change the way we design ICs? The objective of the panel is to explore these issues and formulate a list of critical issues that need to be addressed by the EDA community to enable successful scaling of CMOS into the sub-90nm era.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.899

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)0.0010.000

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2003
Admission routes1
Has abstractyes

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