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Record W2030254671 · doi:10.1145/2145694.2145744

EmPower

2012· article· en· W2030254671 on OpenAlexaff
Sundaram Ananthanarayanan, Chirag Ravishankar, Siddharth Garg, Andrew Kennings

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMPSoCComputer scienceEmbedded systemEmulationDesign space explorationFrequency scalingComputer architectureSystem on a chipPower managementField-programmable gate arrayClock gatingBenchmarkingSoftwareMulti-core processorPower gatingPower (physics)Operating systemEngineeringJitter

Abstract

fetched live from OpenAlex

Dynamic power management for multi-core system on chip (MPSoC) platforms has become an increasingly critical design problem. In this paper, we present EmPower, an FPGA based emulation, validation and prototyping framework for dynamic power management research targeted at MPSoC platforms. EmPower supports two advanced power management features -- per-core dynamic frequency scaling and clock gating, and power-aware thread migration. We also provide two fully-functional parallel applications for benchmarking -- video encoding and software-defined radio. Our experimental results indicate that EmPower provides up to 36 -- improvement in run-time compared to cycle-accurate software simulations, and enables accurate and efficient exploration of the design space of power management algorithms.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.177

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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2012
Admission routes1
Has abstractyes

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