MétaCan
Menu
Back to cohort
Record W1989578025 · doi:10.1109/tc.2012.163

Post-silicon code coverage for multiprocessor system-on-chip designs

2012· article· en· W1989578025 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

VenueIEEE Transactions on Computers · 2012
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of British Columbia
FundersSemiconductor Research Corporation
KeywordsComputer scienceSiliconSilicon chipMultiprocessingMeasure (data warehouse)ChipCorrectnessEmbedded systemCode (set theory)BootingReliability engineeringParallel computingSet (abstract data type)EngineeringMaterials scienceOptoelectronicsTelecommunicationsAlgorithm

Abstract

fetched live from OpenAlex

Effective techniques for post-silicon validation are required to better evaluate functional correctness of increasingly complex multi and many-core SoCs. However, there is little data evaluating the coverage of post-silicon validation efforts on industrial-scale designs. In this paper, we address this knowledge gap by instrumenting a nontrivial SoC with on-chip coverage monitors to measure the coverage achieved by typical post-silicon validation tests, such as booting the operating system (OS). We compare coverage achieved pre and post-silicon, and also measure the area overhead required to monitor post-silicon coverage. Our results show that the typical test of booting the OS often achieves high coverage, well correlated to what is achieved by pre-silicon directed tests, but in some blocks the coverage can be low or markedly different between pre and post-silicon, highlighting the importance of post-silicon validation in general and post-silicon coverage measurement in particular.

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)0.000
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: Empirical · 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 communication0.0000.001
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 head0.043
GPT teacher head0.261
Teacher spread0.218 · 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