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On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators
dc.creator | Díaz-Lobo, Pablo | es |
dc.creator | Liñán-Cembrano, Gustavo | es |
dc.creator | Rosa Utrera, José Manuel de la | es |
dc.date.accessioned | 2024-03-05T08:39:18Z | |
dc.date.available | 2024-03-05T08:39:18Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Díaz-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.issn | 1549-8328 | es |
dc.identifier.issn | 1558-0806 | es |
dc.identifier.uri | https://hdl.handle.net/11441/155819 | |
dc.description.abstract | This 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.sponsorship | MCIN/AEI/10.13039/501100011033 and European Union by ERDF PID2019-103876RB-I00 and PID2022-138078OB-I00 | es |
dc.description.sponsorship | Junta de Andalucía P20-00599 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | IEEE Transactions on Circuits and Systems I: Regular Papers. | |
dc.subject | Design Automation | es |
dc.subject | Optimization | es |
dc.subject | Neural Networks | es |
dc.subject | Analog-to-Digital Converters | es |
dc.subject | Sigma-Delta | es |
dc.title | On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo | es |
dc.relation.projectID | PID2019-103876RB-I00 | es |
dc.relation.projectID | PID2022-138078OB-I00 | es |
dc.relation.projectID | P20-00599 | es |
dc.relation.publisherversion | https://dx.doi.org/10.1109/TCSI.2023.3338056 | es |
dc.identifier.doi | 10.1109/TCSI.2023.3338056 | es |
dc.journaltitle | IEEE Transactions on Circuits and Systems I: Regular Papers | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |
dc.contributor.funder | Junta de Andalucía | es |
Ficheros | Tamaño | Formato | Ver | Descripción |
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10.1109TCSI.2023.3338056.pdf | 2.954Mb | [PDF] | Ver/ | Versión aceptada |
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