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Record W1917361244 · doi:10.1109/mwscas.1989.102039

Optimal allocation of multiport memories in datapath synthesis

2003· article· en· W1917361244 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDatapathComputer scienceRegister allocationSet (abstract data type)HeuristicRange (aeronautics)High-level synthesisArithmeticTheoretical computer scienceParallel computingProgramming languageEmbedded systemMathematicsArtificial intelligenceEngineeringField-programmable gate array

Abstract

fetched live from OpenAlex

In order to optimally allocate multiport memories in datapath synthesis, registers are simultaneously assigned to a configuration of several memories; this gives a more uniform distribution of register activity across the memories and usually provides a more compact assignment, allowing even fewer ports and fewer module interconnections. The LP model is extended, and a fast heuristic approach that searches for an allocation is presented. This allocation algorithm is part of a more general design tool that generates alternative multiport memory configurations and allows their rapid exploration. The designer specifies a range of design parameters, and can control the thoroughness of the search. Within the limits set by the designer, the program finds the minimum cost configuration having a feasible register allocation. The components of this system are described, and some examples of its use are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.671
Threshold uncertainty score0.234

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 head0.018
GPT teacher head0.256
Teacher spread0.237 · 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