Article
On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators
Author/s | Díaz-Lobo, Pablo
Liñán-Cembrano, Gustavo Rosa Utrera, José Manuel de la |
Department | Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo |
Publication Date | 2024 |
Deposit Date | 2024-03-05 |
Published in |
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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 ... 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. |
Funding agencies | Ministerio de Ciencia e Innovación (MICIN). España Agencia Estatal de Investigación. España European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) Junta de Andalucía |
Project ID. | PID2019-103876RB-I00
PID2022-138078OB-I00 P20-00599 |
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. |
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