MétaCan
Menu
Back to cohort
Record W2528910556 · doi:10.1109/async.2016.12

Finding Glitches Using Formal Methods

2016· article· en· W2528910556 on OpenAlex
Peng Yan, Ian W. Jones, Mark R. Greenstreet

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlitchComputer scienceFormal verificationFormal equivalence checkingBoolean satisfiability problemSatisfiabilityStatement (logic)Domain (mathematical analysis)Combinational logicLogic simulationProperty (philosophy)Computer engineeringAlgorithmFormal methodsElectronic circuitTheoretical computer scienceLogic gateProgramming languageMathematicsDetectorElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

The increasing scale and complexity of integrated circuits leads to many departures from a pure, synchronous design methodology. Clock-domain crossings, multi-cycle paths, and circuits for test with long combinational logic delays introduce vulnerabilities for glitch-related failures. Conventional simulation techniques can miss glitches because of the large number of value and timing scenarios. We have tried several commercially available tools but have not found a comprehensive solution. This paper presents a concise statement of what it means for a logic circuit to be "glitch free". This property can be verified using satisfiability solvers. We present our implementation using the ACL2 theorem proving system and some experimental results.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.112
GPT teacher head0.367
Teacher spread0.255 · 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

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicVLSI and Analog Circuit TestingFrench-language works237,207