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Record W2034272662 · doi:10.1115/interpack2009-89188

Development of a Numerical Model for Non-Uniformly Powered Die to Improve Both Thermal and Device Clock Performance

2009· article· en· W2034272662 on OpenAlexaff
Saket Karajgikar, Dereje Agonafer, Kanad Ghose, Bahgat Sammakia, Cristina H. Amon, Gamal Refai-Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsAdvanced Micro Devices (Canada)University of Toronto
Fundersnot available
KeywordsMicroprocessorJunction temperatureReliability (semiconductor)Power (physics)Die (integrated circuit)Computer scienceCacheController (irrigation)Memory controllerMaterials scienceEmbedded systemElectronic engineeringParallel computingComputer hardwareEngineeringOperating system

Abstract

fetched live from OpenAlex

Integration of different functional components such as level two (L2) cache memory, high-speed I/O interfaces, memory controller, etc. has enhanced microprocessor performance. In this architecture, certain functional units on the microprocessor dissipate a significant fraction of the total power while other functional blocks dissipate little or no power. This highly non-uniform power distribution results in a large temperature gradient with localized hot spots that may have detrimental effect on computer performance and product reliability as well as yield. Moving the functional blocks may reduce the junction temperature but can also affect the performance by a factor as high as 35%. In this paper, multi-objective optimization is performed to minimize the junction temperature without significantly altering the computer performance. From the results, the minimum and the maximum temperature was 82.4°C and 94.5°C with a corresponding penalty on the performance of 35% and 0% respectively. The optimized location of the functional blocks resulted in a temperature of 83.2°C for a performance loss of 5%.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.857

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)0.0000.000

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 head0.011
GPT teacher head0.219
Teacher spread0.207 · 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 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

Citations6
Published2009
Admission routes1
Has abstractyes

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