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Record W2972465395 · doi:10.1109/cjece.2019.2897624

Fault-Tolerant DSP Core Datapath Against Omnidirectional Spatial Impact of SET

2019· article· en· W2972465395 on OpenAlexvenueno aff
Deepak Kachave, Anirban Sengupta

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsnot available
Fundersnot available
KeywordsDatapathTransient (computer programming)Digital signal processingComputer scienceOmnidirectional antennaFault (geology)Fault toleranceEmbedded systemReal-time computingDistributed computingComputer hardwareTelecommunicationsSeismology

Abstract

fetched live from OpenAlex

Fault due to the single-event transient (SET) on digital signal processing (DSP) cores has not been thoroughly studied in the research community. Moreover, as the technology scaling becomes more intense, the impact of transient fault now becomes visible both in temporal and spatial domains. Few techniques have been presented in the literature that simultaneously address temporal and the spatial effects of transient fault. However, none of these approaches consider the omnidirectional spatial propagation of transient fault. Furthermore, these approaches have failed to consider the impact of transient fault on switching elements. This calls for methodology to tackle both the temporal and omnidirectional spatial effects of the transient fault on DSP cores. The proposed approach tackles these issues and presents a novel methodology to generate a low-cost fault-tolerant DSP core datapath against temporal (kc-cycle) and omnidirectional spatial (km-unit) impacts of SET.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Electrical and Computer EngineeringSame topicRadiation Effects in ElectronicsFrench-language works237,207