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Record W2023900486 · doi:10.1145/2145694.2145737

A configurable architecture to limit wakeup current in dynamically-controlled power-gated FPGAs

2012· article· en· W2023900486 on OpenAlexaff
Assem A. M. Bsoul, Steven J. E. Wilton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsField-programmable gate arrayPower gatingComputer sciencePower (physics)Embedded systemVoltage droopIdleOverhead (engineering)VoltageComputer hardwareElectrical engineeringEngineeringTransistorVoltage source

Abstract

fetched live from OpenAlex

A dynamically-controlled power-gated (DCPG) FPGA architecture has recently been proposed to reduce static energy dissipation during idle periods. During a power mode transition from an off state to on state, the wakeup current drawn from power supplies causes a voltage droop on the power distribution network of a device. If not handled appropriately, this current and the associated voltage droop could cause malfunction of the design and/or the device. In DCPG FPGAs, the amount of wakeup current is not known beforehand as the structures of power-gated modules are application dependent; thus, a configurable solution is required to handle wakeup current. In this paper we propose a programmable wakeup architecture for DCPG FPGAs. The proposed solution has two levels: a fixed intra-region level and a configurable inter-region level. The architecture ensures that a power-gated module can be turned on such that the wakeup current constraints are not violated. We study the area and power overheads of the proposed solution. Our results show that the area overhead of the proposed inrush current limiting architecture is less than 2% for a power gating region of size 3x3 or 4x4 tiles, and the leakage power saved is more than 85% in a region of size 4x4 tiles.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.214
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.

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

Citations9
Published2012
Admission routes1
Has abstractyes

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