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Record W2021258236 · doi:10.1109/ccece.2012.6334961

Cost-performance analysis of component architectural designs for Dynamic Partially Reconfigurable Systems

2012· article· en· W2021258236 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
TopicEmbedded Systems Design Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceComponent (thermodynamics)Computer architectureArchitectureSystems designSelection (genetic algorithm)Embedded systemDistributed computingSoftware engineering

Abstract

fetched live from OpenAlex

Dynamic Partially Reconfigurable Computing(DPRC) systems have received significant attention in recent years as a potential alternative to traditional computing system designs; such systems implement much of their functionality in the form of virtual components, represented by configuration bit-streams. However, for such systems to be adopted as viable mainstream solutions to system design, a number of problems and challenges must be addressed. One such challenge is the decision criteria for the selection of architectures for the components which make up such systems. This study explores the importance a cost-efficiency factor (CEF) has in the design of virtual components in embedded reconfigurable systems. Dedicated hardware, software, and hybrid architectures were analyzed for two video applications. The resulting analysis demonstrates that the selection of architectures for virtual components can benefit from considerations of the CEF and time-to-market associated with each architecture.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.074
GPT teacher head0.309
Teacher spread0.235 · 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

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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