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Record W4244102356 · doi:10.1109/dac.2002.1012607

High-level specification and automatic generation of IP interface monitors

2002· article· en· W4244102356 on OpenAlexaff
Marcio T. Oliveira, Alan J. Hu

Bibliographic record

VenueProceedings 2002 Design Automation Conference (IEEE Cat. No.02CH37324) · 2002
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceVerilogVHDLBlock (permutation group theory)Embedded systemInterface (matter)Protocol (science)Formal verificationSystem on a chipHardware description languageChipProgramming languageOperating systemField-programmable gate array

Abstract

fetched live from OpenAlex

A central problem in functional verification is to check that a circuit block is producing correct outputs while enforcing that the environment is providing legal inputs. To attack this problem, several researchers have proposed monitor-based methodologies, which offer many benefits. This paper presents a novel, high-level specification style for these monitors, along with a linear-size, linear-time translation algorithm into monitor circuits. The specification style naturally fits the complex, but well-specified interfaces used between IP blocks in systems-on-chip. To demonstrate the advantage of our specification style, we have specified monitors for various versions of the Sonics OCP protocol as well as the AMBA AHB protocol, and have developed a prototype tool that automatically translates specifications into Verilog or VHDL monitor 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.190
GPT teacher head0.292
Teacher spread0.102 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueProceedings 2002 Design Automation Conference (IEEE Cat. No.02CH37324)Same topicFormal Methods in VerificationFrench-language works237,207