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Record W2152242710 · doi:10.1109/iscas.2007.378652

A Dual-Threshold FPGA Routing Design for Subthreshold Leakage Reduction

2007· article· en· W2152242710 on OpenAlexaff
Rodrigo Jaramillo-Ramirez, Mohab Anis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSubthreshold conductionLeakage (economics)Computer scienceField-programmable gate arrayTransistorControl reconfigurationDramRouting (electronic design automation)Very-large-scale integrationEmbedded systemElectronic engineeringElectrical engineeringComputer hardwareEngineeringVoltage

Abstract

fetched live from OpenAlex

Field-programmable gate arrays are attractive candidates for wireless applications due to their reconfiguration capability and high performance development. However, sub-threshold leakage power in modern FPGAs has become a critical obstacle for these devices in entering to this application domain. Subthreshold leakage current of programmable interconnections is responsible for most of the static power dissipation. This work proposes and evaluates ten routing designs based on the dual-threshold technique to reduce leakage power. Alternating between buffers and pass transistors, we analyze the percentage constitution of low-V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> and high-V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> transistors as a function of the leakage reduction and delay increment tradeoff. By routing a suite of MCNC benchmark circuits, we show that an average savings of about 28.83% (as high as 48.46%) in total interconnect leakage can be obtained with 8.73% worst case average delay penalty.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.239
Teacher spread0.213 · 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 designBench or experimental
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

Citations2
Published2007
Admission routes1
Has abstractyes

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