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Record W2137663768 · doi:10.1109/tvlsi.2009.2017443

Low-Power Programmable FPGA Routing Circuitry

2009· article· en· W2137663768 on OpenAlexaff
Jason H. Anderson, Farid N. Najm

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsField-programmable gate arraySleep modeRouting (electronic design automation)Computer sciencePower analysisEmbedded systemDynamic demandPower (physics)Electronic engineeringEngineeringPower consumptionCryptography

Abstract

fetched live from OpenAlex

We consider circuit techniques for reducing field-programmable gate-array (FPGA) power consumption and propose a family of new FPGA routing switch designs that are programmable to operate in three different modes: high-speed, low-power, or sleep. High-speed mode provides similar power and performance to traditional FPGA routing switches. In low-power mode, speed is curtailed in order to reduce power consumption. Leakage is reduced by 28%-52% in low-power versus high-speed mode, depending on the particular switch design selected. Dynamic power is reduced by 28%-31% in low-power mode. Leakage power in sleep mode, which is suitable for unused routing switches, is 61%-79% lower than in high-speed mode. Each of the proposed switch designs has a different power/area/speed tradeoff. All of the designs require only minor changes to a traditional routing switch and involve relatively small area overhead, making them easy to incorporate into current commercial FPGAs. The applicability of the new switches is motivated through an analysis of timing slack in industrial FPGA designs. It is observed that a considerable fraction of routing switches may be slowed down (operate in low-power mode), without impacting overall design performance.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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 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

Citations51
Published2009
Admission routes1
Has abstractyes

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