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Record W2077890035 · doi:10.1109/aspdac.2013.6509685

Reviving erroneous stability-based clock-gating using partial Max-SAT

2013· article· en· W2077890035 on OpenAlexaff
Long Bao Le, Dipanjan Sengupta, Andreas Veneris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDebuggingComputer scienceClock gatingPower gatingBenchmark (surveying)Very-large-scale integrationProcess (computing)ImplementationEmbedded systemComputer engineeringElectronic engineeringClock skewEngineeringTransistorClock signalVoltageElectrical engineering

Abstract

fetched live from OpenAlex

The conflicting yet increasing demand for high performance and low power in multi-functional chips has pushed techniques for power reduction to the forefront of VLSI design. Although recent developments have automated most of the low power implementations, designers often manually modify the circuit in order to achieve further power savings. This human intervention is often paved with many errors that are bound to typical logic functional failures. Debugging these errors can be a resource intensive process that requires considerable manual effort. This discourages engineers and achieving power savings at the micro level of the design sometimes remains unrealized. This paper proposes a novel debugging methodology to rectify erroneous clock-gating implementations. With the use of Partial Max-SAT, the method localizes and rectifies the design error introduced in the circuit during a clock-gating implementation. The net effect of the proposed methodology leads to shorter debug time ensuring additional power savings. Extensive experiments on benchmark circuits confirm the effectiveness of the approach.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.203
Teacher spread0.182 · 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 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

Citations5
Published2013
Admission routes1
Has abstractyes

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