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Record W1972633898 · doi:10.4271/2015-01-0186

Improved ECU End of Line Testing using Multicore Microcontroller

2015· article· en· W1972633898 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

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2015
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsMulti-core processorMicrocontrollerComputer scienceEmbedded systemLine (geometry)Operating system

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">End of Line tests are brief set of tests intended to evaluate ECU's in order to ensure correct functioning of its intended functionality.</div><div class="htmlview paragraph">As these tests are executed on the production line, available time to perform these tests is limited. On one hand, faster production demands require these tests and its framework to be designed in a time optimized manner. On the other hand, increase in ECU functionality translates to an increase in test's functional coverage, requiring more time. Therefore the time taken to execute the tests reaches a critical point in overall ECU production.</div><div class="htmlview paragraph">Availability of multicore microcontrollers with increase in clock speed can increase the performance of end of line tests, but design challenges e.g. synchronization do not guarantee a linear performance increase. Therefore, design of test execution framework is absolutely critical to increase performance of test execution.</div><div class="htmlview paragraph">This paper attempts to provide a framework design that uses multicore based microcontroller solution to increase test execution.</div><div class="htmlview paragraph">The paper details out currently available test setup, followed by a design analysis to outline critical areas limiting EOL performance. Subsequently, mechanisms using multicore based solutions such as dynamic task allocation will be detailed to overcome these limitations and new requirements for the same shall be specified. The paper will also provide information on the implementation and comparison results with a single core solution. As the concluding step, future challenges shall be outlined.</div><div class="htmlview paragraph">The microcontroller mentioned in this paper refers to Infineon 32-bit Tricore™ MCU, TC178x and AURIX™.</div></div>

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
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 head0.038
GPT teacher head0.275
Teacher spread0.237 · 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