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Record W1975899710 · doi:10.5555/2616606.2616698

Efficient transient thermal simulation of 3D ICs with liquid-cooling and through silicon vias

2014· article· en· W1975899710 on OpenAlexaff
Alain Fourmigue, Giovanni Beltrame, Gabriela Nicolescu

Bibliographic record

VenueDesign, Automation, and Test in Europe · 2014
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTransient (computer programming)Three-dimensional integrated circuitSpeedupThermalComputer coolingIntegrated circuitSiliconThrough-silicon viaDissipationElectronic engineeringThermal management of electronic devices and systemsMaterials scienceComputer scienceWork (physics)Mechanical engineeringEngineeringParallel computingOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Three-dimensional integrated circuits (3D ICs) with advanced cooling systems are emerging as a viable solution for many-core platforms. These architectures generate a high and rapidly changing thermal flux. Their design requires accurate transient thermal models. Several models have been proposed, either with limited capabilities, or poor simulation performance. This work introduces an efficient algorithm based on the Finite Difference Method to compute the transient temperature in liquid-cooled 3D ICs. Our experiments show a 5x speedup versus state-of-the-art models, while maintaining the same level of accuracy, and demonstrate the effect of large through silicon vias arrays on thermal dissipation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations20
Published2014
Admission routes1
Has abstractyes

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