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Record W2751462251 · doi:10.1109/tcad.2017.2747999

Methodologies for Diagnosis of Unreachable States via Property Directed Reachability

2017· article· en· W2751462251 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 Computer-Aided Design of Integrated Circuits and Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLivenessReachabilityComputer scienceDebuggingSet (abstract data type)Property (philosophy)Relation (database)State spaceState (computer science)Theoretical computer scienceModel checkingUnobservableAlgorithmDistributed computingProgramming languageMathematicsData mining

Abstract

fetched live from OpenAlex

In the modern design cycle, substantial manual effort is required to correct failed liveness properties due to the limited availability of automated tools. To address this limitation, this paper introduces two techniques to diagnose register transfer level errors that manifest in the form of erroneously unreachable states, which represent a common form of liveness property failure. The first uses steps of reachable state-space over-approximation and traditional debugging to compute a subset of the solutions that make a target state reachable. The second solves a series of unbounded model checking problems using an enhanced model of the circuit's transition relation to compute the complete solution set to the problem. The proposed techniques are complementary to each other and present the user with a configurable tradeoff between runtime and resolution of the returned solution set. Empirical results on OpenCores and HWMCC'15 circuits confirm the effectiveness of the approaches and demonstrate the tradeoffs between them.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.059
GPT teacher head0.274
Teacher spread0.215 · 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