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Record W2163456592 · doi:10.1109/icee.2008.4553920

Implementing a cost-effective run-time reconfigurable system for stream applications

2008· article· en· W2163456592 on OpenAlexafffund
Pil Woo Chun, Jamin Islam, Valeri Kirischian, Lev Kirischian

Bibliographic record

Venue2008 Second International Conference on Electrical Engineering · 2008
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsToronto Metropolitan University
FundersOntario Centres of Excellence
KeywordsComputer scienceReal-time computingEmbedded systemDistributed computing

Abstract

fetched live from OpenAlex

In today’s consumer electronics the devices are crossing the boundaries by integrating the variety of functionalities to increase their market shares. As a result, the primary focus of designing consumer electronics lies upon implementation of cost-effective, multi-task and multi-mode operations. We suggest a methodology for a cost-effective implementation of the reconfigurable system based on run-time reconfigurable FPGAs. The methodology focuses on extraction of the architecture that remains static throughout the lifetime of the application. The static architecture is constructed by analyzing the tasks associated with event(s) and distinguishing the level of task that needs to be in hardware. With the given architecture the system maintains inter-communication and only replaces the tasks that are necessary via reconfiguration. The idea of the proposed system is employed on the image processing of an autonomous satellite docking system because of tight physical limitations and high speed requirements. Obtained results prove that the run-time reconfigurable system architecture can provide a very efficient way to mosaic the micro-architecture that reuses limited hardware resources in time.

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 categoriesMeta-epidemiology (narrow)
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 score0.973
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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 designSimulation or modeling
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

Citations1
Published2008
Admission routes2
Has abstractyes

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