MétaCan
Menu
Back to cohort
Record W2536590467 · doi:10.1109/icm.2011.6177347

A new hardware abstraction-based framework to cope with analog design challenges

2011· article· en· W2536590467 on OpenAlexaff
Sabeur Lafi, Ammar B. Kouki, J. Belzile

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceAbstractionMicroelectronicsVariety (cybernetics)Context (archaeology)Computer architectureWirelessElectronic design automationReliability (semiconductor)Design processDesign technologyDesign methodsProcess (computing)Embedded systemSystems engineeringTelecommunicationsEngineeringWork in processElectrical engineering

Abstract

fetched live from OpenAlex

Recent years have been marked by the emergence of a wide variety of communication standards. The prevailing tendency is for global network convergence and wireless ubiquity. However, this leads to challenging design issues that have to be surmounted. Design methodologies and computer-aided design (CAD) tools currently in use suffer from significant shortcomings especially in radio-frequency (RF) microelectronics. Dependence of the design process on the underlying technology and the absence of high-level abstractions do not help designers. In this context, we propose in this paper a new design framework based on hardware abstraction in order to enhance design reliability and efficiency. Then, we discuss how this design framework can be a suitable response to the lacks of the conventional design schemes in RF and microelectronic systems.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.064
GPT teacher head0.225
Teacher spread0.161 · 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 designSimulation or modeling
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

Citations1
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicVLSI and FPGA Design TechniquesFrench-language works237,207