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Record W2547639812 · doi:10.1109/ccece.2016.7726658

A novel gate grading approach for soft error tolerance in combinational circuits

2016· article· en· W2547639812 on OpenAlexaff
Mohammad Saeed Ansari, Ali Mahani, Jie Han, B.F. Cockburn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCombinational logicRedundancy (engineering)Computer scienceSoft errorLogic gateElectronic engineeringElectronic circuitCircuit reliabilityVery-large-scale integrationFault toleranceOverhead (engineering)Reliability engineeringReliability (semiconductor)EngineeringPower (physics)Embedded systemElectrical engineeringAlgorithm

Abstract

fetched live from OpenAlex

Continuous reduction in the minimum feature size of semiconductor devices and the supply voltages in advanced VLSI logic circuits has made those circuits more susceptible to soft errors. Hence, several fault tolerance techniques have been proposed in the literature to protect combinational circuits against single event transients (SETs). These fault tolerance techniques are based mainly on hardware redundancy and therefore they come at the cost of significant area and power overhead. In this paper, a novel gate grading approach is proposed to prioritize gates based on their influence on the circuit's reliability. Specifically, different masking factors are taken into account and the gates with the lowest masking capabilities are identified so that they can be hardened first. Since the gates with higher priorities affect the circuit's reliability more significantly, protecting those gates increases the circuit's reliability with the least required area and power overhead.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.320

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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