On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators
This paper presents a high-level synthesis methodology for Sigma-Delta Modulators (Sigma Delta Ms) that combines behavioral modeling and simulation for performance evaluation, and Artificial Neural Networks (ANNs) to generate high-level designs variables for the required specifications. To this end,...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/385837 |
| Acceso en línea: | http://hdl.handle.net/10261/385837 https://api.elsevier.com/content/abstract/scopus_id/85179816067 |
| Access Level: | acceso abierto |
| Palabra clave: | Analog-to-digital converters Design automation Neural networks Optimization Sigma-delta modulation |
| Sumario: | This paper presents a high-level synthesis methodology for Sigma-Delta Modulators (Sigma Delta Ms) that combines behavioral 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 performance metrics, generated from accurate behavioral simulations of different kinds of Sigma Delta 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 proposed solution works in two steps. First, for a given set of specifications, a trained classifier proposes one of the available Sigma Delta text{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. |
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