High-level optimization of ΣΔ modulators using multi-objetive evolutionary algorithms

Velasco-Jimenez, Manuel; Castro-López, R.; Rosa, José M. de la

doi:10.1109/iscas.2016.7527541

Cited by 9 publications

(2 citation statements)

References 10 publications

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“…Typically, the sizing process is carried out by using an optimization engine, which guides the simulator through the exploration of the design space to find the optimum design solution. Different optimization algorithms have been proposed to this end, including genetic, simulated annealing, or multi-objective evolutionary Pareto fronts, to cite a few [11], [16], [19], [22], [23]. Regardless of the abstraction level, the optimization method involves defining a design-oriented cost function or a Figure of Merit (FOM) and minimizing it [24].…”

Section: A Conventional Optimization-based Design Methodsmentioning

confidence: 99%

Design Automation of Analog and Mixed-Signal Circuits Using Neural Networks—A Tutorial Brief

Linán-Cembrano,

Lourenço,

Horta

et al. 2024

IEEE Trans. Circuits Syst. II

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This tutorial brief shows how Artificial Neural Networks (ANNs) can be used for the optimization and automated design of analog and mixed-signal circuits. A survey of conventional and computational-intelligence design methods is given as a motivation towards using ANNs as optimization engines. A step-by-step procedure is described explaining the key aspects to consider in our approach, such as dataset preparation, ANNs modeling, training, and optimization of network hyperparameters. As an application, two case studies at different hierarchy levels are presented. The first one is the system-level sizing of Sigma-Delta Modulators (Σ∆Ms), where ANNs are combined with behavioral simulations to generate valid circuit-level design variables for a given set of specifications. The second example combines ANNs with electrical simulators to optimize the circuitlevel design of operational transconductance amplifiers. The results validate the presented approach and show its benefits with respect to prior art on synthesis methods of analog and mixed-signal circuits and systems.

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Section: A Conventional Optimization-based Design Methodsmentioning

confidence: 99%

Design Automation of Analog and Mixed-Signal Circuits Using Neural Networks—A Tutorial Brief

Linán-Cembrano,

Lourenço,

Horta

et al. 2024

IEEE Trans. Circuits Syst. II

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show abstract

“…The most general approach is based on the so-called optimization-based synthesis method, where an optimizer is combined with a performance evaluator (usually a simulator) to find the optimum design. A number of optimization algorithms have been proposed to this end, including genetic, simulated annealing, or multi-objective evolutionary Pareto fronts, to cite a few [3], [4], [7], [10].…”

Section: Introductionmentioning

confidence: 99%

On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators

Díaz-Lobo,

Liñán-Cembrano,

de la Rosa

2024

IEEE Trans. Circuits Syst. I

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This paper presents a high-level synthesis methodology for Sigma-Delta Modulators (Σ∆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 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 proposed 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.

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AI-Powered Enhancement of Sigma Delta ADC: The Role of Artificial Neural Network-Based Optimization Systems

Mounika,

Ahmad,

Hussain

et al. 2024

IEEE Access

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High-level optimization of ΣΔ modulators using multi-objetive evolutionary algorithms

Cited by 9 publications

References 10 publications

Design Automation of Analog and Mixed-Signal Circuits Using Neural Networks—A Tutorial Brief

Design Automation of Analog and Mixed-Signal Circuits Using Neural Networks—A Tutorial Brief

On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators

AI-Powered Enhancement of Sigma Delta ADC: The Role of Artificial Neural Network-Based Optimization Systems

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