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Record W4234695468 · doi:10.1109/icpp.2004.1327958

Architecture and implementation of chip multiprocessors: custom logic components and software for rapid prototyping

2004· article· en· W4234695468 on OpenAlexaff
N. Manjikian, Jin Huang, J. Reed, N. Cordeiro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceEmbedded systemExecutableComputer architectureRapid prototypingSoftware prototypingProgrammable Array LogicSoftwareSimple programmable logic deviceLogic synthesisComputer hardwareOperating systemLogic familySoftware developmentLogic gateEngineering

Abstract

fetched live from OpenAlex

This work describes components and software tools in support of rapid prototyping in programmable logic for research on chip multiprocessors. Contemporary programmable logic chips offer considerable on-chip logic and memory resources. Prototyping of systems in programmable logic chips is faster and less costly than full-custom chip design. The first contribution that is described in this paper is a collection of original research-oriented logic components that provides processor, memory, and interconnect functionality for rapid prototyping. Because these are original components, and not proprietary vendor-supplied components, they may be arbitrarily extended and modified to suit research needs. The second contribution is a set of enhanced software tools for generating executable code. The third contribution is user-configurable software for testing and evaluating prototype chip multiprocessor implementations in hardware. In addition to describing these contributions, this paper provides results from implementing and testing prototype components and complete chip multiprocessors, including simulation waveforms, logic chip resource utilization, and observations of hardware operation.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.904
Threshold uncertainty score0.287

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 head0.023
GPT teacher head0.294
Teacher spread0.272 · 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 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

Citations2
Published2004
Admission routes1
Has abstractyes

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