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Record W1907251739 · doi:10.1109/iscas.1993.394063

Resynthesis and retiming of synchronous sequential circuits

2002· article· en· W1907251739 on OpenAlexaff
S. Lejmi, Bożena Kamińska, Édouard Wagneur

Bibliographic record

Venue1993 IEEE International Symposium on Circuits and Systems · 2002
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRetimingSequential logicComputer scienceBipartite graphElectronic circuitCombinational logicAlgorithmGraphLogic synthesisGeneralizationParallel computingLogic gateMathematicsTheoretical computer scienceEngineering

Abstract

fetched live from OpenAlex

Retiming is a technique used in synchronous sequential circuits to move the registers across the circuit in order to minimize the cycle time or the number of registers used. Peripheral retiming is extended retiming in which all registers are moved (if possible) to the peripheral edges, where combinational logic optimization techniques can be used. A generalization of this method is proposed. It consists in moving the maximum number of registers to the peripheral edges. This approach may then also be applied to all sequential circuits for which the usual peripheral retiming concept does not hold. The authors' model of the circuit is a bipartite graph, and it is proved that the resynthesizing problem is equivalent to a linear optimization problem in this graph. A new algorithm for the optimization of sequential circuits is proposed.>

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.223
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 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
Published2002
Admission routes1
Has abstractyes

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