MétaCan
Menu
Back to cohort
Record W2115695824 · doi:10.1109/imtc.2007.379055

VLSI Circuit Test Vector Compression Technique

2007· article· en· W2115695824 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

VenueConference proceedings - IEEE Instrumentation/Measurement Technology Conference · 2007
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLossless compressionComputer scienceComputer hardwareVery-large-scale integrationTest vectorData compressionChipEmbedded systemOverhead (engineering)Benchmark (surveying)Test compressionAutomatic test pattern generationTest setElectronic circuitEngineeringAlgorithmElectrical engineering

Abstract

fetched live from OpenAlex

A new test vector compression method for VLSI circuit testing is presented in this paper. The technique is essentially software-based, where a program is loaded into the on-chip processor memory along with the compressed test data sets. To reduce the on-chip storage area and testing time, the large volume of test data is first compressed before downloading into the on-chip processor. The proposed method utilizes a set of adaptive coding techniques for achieving lossless compression. The compression program need not be loaded into the embedded processor, as only the decompression of the test data is is necessary for application by the automatic test equipment (ATE). The technique requires minimal hardware overhead, while the on-chip processor core can be reused for normal operation after testing. The feasibility of the developed approach has been demonstrated through extensive simulation experiments on ISCAS 85 and ISCAS 89 benchmark circuits.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0030.000
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 head0.060
GPT teacher head0.265
Teacher spread0.205 · 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