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Record W2152362440 · doi:10.1145/503048.503059

Integrated retiming and placement for field programmable gate arrays

2002· article· en· W2152362440 on OpenAlexaff
Deshanand P. Singh, Stephen D. Brown

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRetimingComputer scienceField-programmable gate arrayRouting (electronic design automation)Gate arrayCombinational logicParallel computingElectronic circuitAlgorithmLogic gateEmbedded systemEngineering

Abstract

fetched live from OpenAlex

Retiming is a synchronous circuit transformation that can optimize the delay of a synchronous circuit by moving registers across combinational circuit elements. The combinational structure remains unchanged and the observable behavior of the circuit is identical to the original.In this paper, we address the problem of applying retiming techniques to circuits implemented in Field Programmable Gate Arrays (FPGAs). FPGAs contain prefabricated and configurable routing elements that allow us to easily implement a variety of circuits. However this interconnect contributes greatly to the overall delay in the implemented circuit. If a circuit is retimed prior to the placement and routing phases of the CAD flow, then it has no information about the delays introduced by the configurable interconnect. Our fundamental experiment is to determine whether there are any gains in tightly coupling retiming and placement so that the retiming algorithm has some estimate of the routing delays.Specifically, we introduce a post-placement retiming algorithm that understands how to take advantage of FPGA architectural features. This retiming algorithm may introduce extra registers into the circuit. These new registers need to be placed in some location in the FPGA. Retiming register placement is accomplished by a novel incremental clustering and placement algorithm. The incremental algorithm builds upon the placement of the non-retimed circuit to intelligently sift in the newly-introduced registers.In addition, we explore making the placement algorithms "retiming aware." These placement algorithms try to place logic blocks in such a way that the subsequent retiming produces better speed results. These techniques include the identification of retiming-critical cycles during placement.Our experiments show that the integration of retiming with placement results in 19% better clock periods in comparison to the application of retiming before the place and route steps.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.212
Teacher spread0.190 · 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
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

Citations54
Published2002
Admission routes1
Has abstractyes

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