MétaCan
Menu
Back to cohort

Configurable Verification IP for UART

2020· article· en· W3022836086 on OpenAlexaff
Stepan Harutyunyan, Taron Kaplanyan, Artak Kirakosyan, Haykaram Khachatryan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUniversal asynchronous receiver/transmitterComputer scienceVerilogEmbedded systemFunctional verificationIntelligent verificationReusabilityInterface (matter)Computer architectureFormal verificationOperating systemProgramming languageSoftwareField-programmable gate array

Abstract

fetched live from OpenAlex

Verification of Integrated Circuits using Verilog lacks the flexibility and reusability of the environment. System Verilog provides building blocks and OOP concepts to work with. That allows to create much more flexible test environment with reusable components. This paper presents a verification architecture of configurable Verification IP for UART interface. The Verification IP presented in this paper provides complete functionality of an operating UART interface and can be used to test any UART device. A functional coverage model has been developed to determine if the verification process covers all possible scenarios or not. Each testcase reports coverage which is later used to analyze the effectiveness of the testcase. Full coverage has been achieved using both random and directed test cases. The coding is done using System Verilog and the simulation is done using VCS.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.038
GPT teacher head0.229
Teacher spread0.191 · 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
GenreEmpirical

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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicPhysical Unclonable Functions (PUFs) and Hardware SecurityFrench-language works237,207