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Record W2140030028 · doi:10.1109/isqed.2006.33

Clock Distribution Architectures: A Comparative Study

2006· article· en· W2140030028 on OpenAlexaff
Chao-Yang Yeh, Gustavo Wilke, H. Chen, S. Reddy, Hoang Dung Nguyen, T. Miyoshi, William W. Walker, Rajeev Murgai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsSkewComputer scienceClock skewLatency (audio)Polygon meshTree (set theory)Static timing analysisDistributed computingArchitectureTiming failureComputer architectureParallel computingEmbedded systemJitterTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

This paper evaluates and compares different clock architectures such as mesh, tree and their hybrids, on several industrial designs. The goal of our study is to gain a quantitative understanding of engineering trade-offs between different architectures with respect to clock skew, latency, timing uncertainty, and power. This understanding will lead to guidelines for determining the best clock architecture for the design specification and constraints. To the best of our knowledge, no work has been published on evaluating and comparing these architectures on real industrial designs. Our study shows that mesh-based architectures are better than tree architectures for skew (< 1ps skew) and are more robust to variations (18% reduction in timing uncertainty as compared to tree). The power penalty associated with a mesh as compared to a tree was found to be between 10-40%. Use of multiple meshes can help reduce the power penalty

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)0.001
metaresearch head score (Gemma)0.005
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: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.219
Teacher spread0.210 · 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
GenreReview

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

Citations50
Published2006
Admission routes1
Has abstractyes

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