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Record W2073770015 · doi:10.1109/tc.2012.275

A Novel Algorithmic Approach to Aid Post-Silicon Delay Measurement and Clock Tuning

2012· article· en· W2073770015 on OpenAlex
Zahra Lak, Nicola Nicolici

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Computers · 2012
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceExploitSiliconStatic timing analysisElectronic engineeringDigital clock managerPruningClock signalEmbedded systemClock skewEngineeringMaterials scienceOptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

The number of speedpaths in modern high-performance designs is in the range of millions and, due to unmodelled electrical effects, they are difficult to be measured accurately before the first silicon samples are available. As a consequence, clock tuning elements are employed to aid the post-silicon clock tuning. However, as the number of these elements continues to grow, it becomes increasingly difficult to determine their configurations in a compute effective manner. In this paper we describe a novel exact algorithm for post-silicon clock tuning, which employs smart pruning techniques that exploit the characteristics of the clock tuning buffers.

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.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.892

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)0.0000.000

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 head0.045
GPT teacher head0.233
Teacher spread0.188 · 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