MétaCan
Menu
Back to cohort
Record W2148414872 · doi:10.21236/ada603903

Integration of Physical Design and Sequential Optimization

2006· report· en· W2148414872 on OpenAlexfundno aff
Philip Chong

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDefense Advanced Research Projects Agency
KeywordsComputer science

Abstract

fetched live from OpenAlex

This work examines the interaction between the physical design of digital integrated circuits and sequential optimization techniques used for performance enhancement. In particular, the integration of floorplanning and placement with retiming and clock skew scheduling is explored. A theoretical result is given which addresses the computational complexity of circuit partitioning under constraints derived from sequential optimization; this motivates the need for heuristic approaches to the related placement problem. Another theoretical result provides a characterization of the feasible retimings of a sequential circuit; this result is used to motivate an effective method for floorplanning integrated with sequential optimization. Practical techniques for using sequential slack to drive standard-cell placement are shown here; experiments demonstrate significant improvement in final design performance using these methods. Another part of this work examines how the role of sequential optimization and physical design changes when the design allows for asynchronous or latency-insensitive communication between modules. A theoretical result relating to the problem of clock tree implementation for clock skew scheduling under process variation is given. Finally an experimental technique for floorplanning using nonlinear programming is demonstrated

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: Methods · Consensus signal: Methods
Teacher disagreement score0.468
Threshold uncertainty score0.516

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 head0.032
GPT teacher head0.266
Teacher spread0.234 · 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
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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicVLSI and FPGA Design TechniquesFrench-language works237,207