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Record W2168274085 · doi:10.1109/mwscas.2005.1594386

Block placement for reduced delay uncertainty in high performance clock distribution networks

2005· article· en· W2168274085 on OpenAlexaff
Atharv Jairath, B. Sivasubramanian, Dimitrios Velenis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsMcGill University
Fundersnot available
KeywordsCritical path methodClock skewComputer scienceSynchronous circuitDigital clock managerBlock (permutation group theory)Delay calculationStatic timing analysisBenchmark (surveying)Clock signalElmore delayAlgorithmPath (computing)Clock networkCombinational logicSequential logicSequence (biology)Process cornersProcess (computing)InterconnectionLogic gateMathematicsEmbedded systemTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Uncertainty in the delay of the clock signal is introduced by a number of factors that affect the clock distribution network, examples of which include process and environmental parameter variations and interconnect noise. The more strict the setup and hold time constraints of a combinational data path, the more sensitive the timing of a data path is to delay uncertainty. A design methodology to reduce the delay uncertainty of the clock signal, particularly at the most critical data paths within a system is presented in this paper. A placement algorithm is utilized that is based on the sequence-pair placement representation which determines the minimal placement area for a set of circuit blocks. The sequence-pair placement approach is modified to prioritize the placement of the blocks containing the critical registers of a circuit. The cost function for placement is also modified. Alternatively to minimal area, the placement objective for the critical blocks is to minimize the non-common portion of the clock tree among the registers of the critical blocks. Once the placement of the critical blocks is determined, the remaining blocks in a circuit are being placed. The proposed methodology is applied to a set of benchmark circuits. It is demonstrated that a reduction of up to 77% on the non-common portion among the critical clock paths can be achieved. The tradeoff of this approach is an increase of up to 9% in the total circuit area.

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: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.966

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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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