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dc.creatorDíaz-Lobo, Pabloes
dc.creatorLiñán-Cembrano, Gustavoes
dc.creatorRosa Utrera, José Manuel de laes
dc.date.accessioned2024-03-05T08:39:18Z
dc.date.available2024-03-05T08:39:18Z
dc.date.issued2024
dc.identifier.citationDíaz-Lobo, P., Liñán-Cembrano, G. y Rosa Utrera, J.M.d.l. (2024). On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators. IEEE Transactions on Circuits and Systems I: Regular Papers. https://doi.org/10.1109/TCSI.2023.3338056.
dc.identifier.issn1549-8328es
dc.identifier.issn1558-0806es
dc.identifier.urihttps://hdl.handle.net/11441/155819
dc.description.abstractThis paper presents a high-level synthesis method ology for Sigma-Delta Modulators (Σ∆Ms) that combines be havioral modeling and simulation for performance evaluation, and Artificial Neural Networks (ANNs) to generate high-level designs variables for the required specifications. To this end, comprehensive datasets made up of design variables and perfor mance metrics, generated from accurate behavioral simulations of different kinds of Σ∆Ms, are used to allow the ANN to learn the complex relationships between design-variables and specifications. Several representative case studies are considered, including single-loop and cascade architectures with single-bit and multi-bit quantization, as well as both Switched-Capacitor (SC) and Continuous-Time (CT) circuit techniques. The pro posed solution works in two steps. First, for a given set of specifications, a trained classifier proposes one of the available Σ∆M architectures in the dataset. Second, for the proposed architecture, a Regression-type Neural Network (RNN) infers the design variables required to produce the requested specifications. A comparison with other optimization methods – such as genetic algorithms and gradient descent – is discussed, demonstrating that the presented approach yields to more efficient design solutions in terms of performance metrics and CPU time.es
dc.description.sponsorshipMCIN/AEI/10.13039/501100011033 and European Union by ERDF PID2019-103876RB-I00 and PID2022-138078OB-I00es
dc.description.sponsorshipJunta de Andalucía P20-00599es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherIEEEes
dc.relation.ispartofIEEE Transactions on Circuits and Systems I: Regular Papers.
dc.subjectDesign Automationes
dc.subjectOptimizationes
dc.subjectNeural Networkses
dc.subjectAnalog-to-Digital Converterses
dc.subjectSigma-Deltaes
dc.titleOn the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulatorses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Electrónica y Electromagnetismoes
dc.relation.projectIDPID2019-103876RB-I00es
dc.relation.projectIDPID2022-138078OB-I00es
dc.relation.projectIDP20-00599es
dc.relation.publisherversionhttps://dx.doi.org/10.1109/TCSI.2023.3338056es
dc.identifier.doi10.1109/TCSI.2023.3338056es
dc.journaltitleIEEE Transactions on Circuits and Systems I: Regular Paperses
dc.contributor.funderMinisterio de Ciencia e Innovación (MICIN). Españaes
dc.contributor.funderAgencia Estatal de Investigación. Españaes
dc.contributor.funderEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)es
dc.contributor.funderJunta de Andalucíaes

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