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Record W2987526256 · doi:10.1109/fpl.2019.00011

Becoming More Tolerant: Designing FPGAs for Variable Supply Voltage

2019· article· en· W2987526256 on OpenAlexaff
Ibrahim Ahmed, Linda L. Shen, Vaughn Betz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLookup tableField-programmable gate arrayVoltageComputer scienceRouting (electronic design automation)ScalingDynamic voltage scalingEnergy consumptionPower (physics)Low-power electronicsEmbedded systemPower consumptionElectrical engineeringEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

With the end of Dennard scaling, FPGA power consumption has become a major concern. While FPGAs are conventionally supplied by a fixed supply voltage (Vdd), recent industrial (SmartVID) and academic solutions (dynamic voltage scaling) have shown significant power savings by scaling the FPGA Vdd on a chip-specific or chip-and application-specific basis. However, FPGAs have historically been designed for fixed-Vdd operation, which raises the question of whether we can design FPGA circuitry that is better suited for voltage scaling. In this work, we show that conventional LUTs are more sensitive to voltage than routing, so we design different LUT circuits that are more tolerant to voltage scaling. Compared to a conventional LUT, our fastest proposed LUT reduces the average critical path delay by 14% and 47% at nominal (0.8 V) Vdd and at reduced (0.6 V) Vdd, respectively. This significant reduction in delay comes at a cost of only 8% FPGA tile area increase. Our proposed LUT designs result in lower energy-delay and energy-delay^2 products at nominal Vdd and below.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.957

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)0.0010.000

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 head0.009
GPT teacher head0.205
Teacher spread0.197 · 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 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

Citations13
Published2019
Admission routes1
Has abstractyes

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