MétaCan
Menu
Back to cohort
Record W2045656654 · doi:10.1109/tvlsi.2014.2377573

Free Razor: A Novel Voltage Scaling Low-Power Technique for Large SoC Designs

2014· article· en· W2045656654 on OpenAlexaff
Yuejian Wu, Sandy Thomson, Han Sun, David Krause, Yu Song, George Kurio

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsOverhead (engineering)System on a chipComputer scienceVoltagePower (physics)Electronic engineeringScalingIntegrated circuit designDynamic voltage scalingError detection and correctionLow-power electronicsFrequency scalingIntegrated circuitChipEmbedded systemElectrical engineeringEngineeringPower consumptionTelecommunicationsAlgorithm

Abstract

fetched live from OpenAlex

This paper proposes a novel voltage scaling low-power design methodology for large system-on-chip (SoC) designs. It scales the supply voltage to a SoC based on operating conditions and bit error rate in a system. It allows occasional timing errors in the circuit and relies on a forward error correction that already exists in the system to correct the errors. As a result, the proposed technique imposes no hardware overhead yet yields significant power savings. More importantly, it does not require any circuit modification based on place and route, thus it is easy to implement and has no impact for time to market. The new technique was implemented in a complex telecom SoC design, and silicon measurements show power savings up to 50% for free.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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
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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Very Large Scale Integration (VLSI) SystemsSame topicLow-power high-performance VLSI designFrench-language works237,207