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Record W2092035831 · doi:10.1109/ccece.2006.277610

Run-Time Reconfigurable Built-in-Self-Test

2006· article· en· W2092035831 on OpenAlexafffund
Rami Abielmona, Voicu Groza, Arkan Khalaf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of Ottawa
FundersCMC Microsystems
KeywordsControl reconfigurationComputer scienceField-programmable gate arrayBuilt-in self-testEmbedded systemVirtexElectronic circuitReconfigurable computingComputer architectureEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper discusses the novel idea of testing digital circuits using run-time reconfigurable techniques, in order to minimize circuit area, as well as test generation and application time. The idea revolves around the dynamic partial reconfiguration of circuits under test, in order to inject stuck-at faults at different locations of the circuit, and uncover both detectable and undetectable faults. The paper presents a practical implementation of run-time reconfigurable methodologies using an actual reconfigurable device, the Xilinx Virtex-II, in the development of a BIST architecture. An updated flow is presented which takes into account the fusion of an embedded processor system and a dynamic partially reconfigurable module

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 categoriesInsufficient payload (model declined to judge)
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 score0.936
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)0.0000.001

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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
GenreEmpirical

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
Published2006
Admission routes2
Has abstractyes

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