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Record W4248348138 · doi:10.1145/500050.500052

Methods for optimizing register placement in synchronous circuits derived using software pipelining techniques

2001· article· en· W4248348138 on OpenAlexaff
Noureddine Chabini, Yvon Savaria

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsPolytechnique MontréalUniversité de Montréal
Fundersnot available
KeywordsSoftware pipeliningComputer scienceScheduleRegister allocationParallel computingElectronic circuitTime complexityShift registerInteger (computer science)SoftwareLinear programmingAlgorithmCompiler

Abstract

fetched live from OpenAlex

A method based on software pipelining has been recently proposed to optimize mono-phase clocked sequential circuits. The resulting circuits are multi-phase clocked sequential circuits, where all clocks have the same period. To preserve functionality of the original circuit, registers must be placed according to a correct schedule. This schedule also ensures the maximum throughput. In that method, it is question of (1) how to determine a schedule that requires the minimum number of registers, and (2) how to place these registers optimally. In this paper, problems (1) and (2) are tackled simultaneously. More precisely, we deal with the problem of determining schedules with the minimum register requirements, where the optimal register placement is done during the schedule determination. To optimally solve that problem, we provide a mixed integer linear program that we use to derive a linear program, which is polynomial-time solvable. We show that the dual of this linear program can be transformed to a minimum cost network flow problem, which can be solved more efficiently. Experimental results confirm the effectiveness of the approach, and show that significant reductions of the number of registers can be obtained. Also, they confirm that the obtained dual formulation can be solved much faster than its primal.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.081
GPT teacher head0.382
Teacher spread0.301 · 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.

Study designOther design
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
Published2001
Admission routes1
Has abstractyes

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