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Record W1998414218 · doi:10.1115/imece2009-11769

Effect of Relocation of Functional Units of a Non-Uniformly Powered Microprocessor on Thermal and Device Clock Performance

2009· article· en· W1998414218 on OpenAlexaff
Saket Karajgikar, Dereje Agonafer, Kanad Ghose, Bahgat Sammakia, Cristina H. Amon, Gamal Refai-Ahmad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsAdvanced Micro Devices (Canada)University of Toronto
Fundersnot available
KeywordsMicroprocessorPentiumMemory controllerComputer sciencePower (physics)Reliability (semiconductor)Junction temperatureEmbedded systemComputer hardwareParallel computingPhysicsSemiconductor memory

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 units 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 effects on computer performance, product reliability, and yield. Moving the functional units may reduce the junction temperature but can also affect performance by as much as 30%. 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 56.6°C and 62.2°C with a corresponding penalty on the performance of 14% and 0% respectively. The numerical analysis was performed for 90 nm Pentium® IV Northwood architecture at 3 GHz clock speed.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.006
GPT teacher head0.195
Teacher spread0.190 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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