Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Rosa, João P. S.; Guerra, Daniel J. D.; Horta, Nuno; Martins, Ricardo; Lourenço, Nuno

doi:10.1007/978-3-030-35743-6

Cited by 18 publications

(8 citation statements)

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“…These neurons are arranged into layers or have their combinations. The neural network consists of 3 layers i.e., the input, the hidden, and the output [28], [29].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Based on these results, DVR-ANN outperforms DVR with PI-PSO, and all DVR-ANN response results are better than DVR response with PI-PSO, meaning that the error produced by DVR-ANN is smaller than DVR with PI-PSO, faster rise time and settling time, as well as less undershoot and overshoot. This is because when datasets are trained on ANN, the network minimizes errors by adjusting the weights on each data's target inputs and outputs, so the ANN controller is more precise [28].…”

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confidence: 99%

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Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Siregar,

Muhammad,

Arief

et al. 2023

IJECE

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Power quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic voltage restorer (DVR) is an efficient and flexible solution for overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on particle swarm optimization (PSO) and artificial neural networks (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to a 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder’s length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with MATLAB-Simulink. The simulation results show that DVR-PSO and DVR-ANN successfully recover voltage sag by supplying voltage at each phase. Based on the results of the analysis shows that DVR-ANN outperforms DVR-PSO in quality and voltage injection into the network.

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“…These neurons are arranged into layers or have their combinations. The neural network consists of 3 layers i.e., the input, the hidden, and the output [28], [29].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

mentioning

confidence: 99%

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Siregar,

Muhammad,

Arief

et al. 2023

IJECE

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“…This section will explore the generative power of Artificial Neural Networks (ANN), focusing on a system developed to assimilate and replicate stylistic regularities in folk music (for systems emulating rock and jazz, see (Dadabots, 2021)). First developed in the mid-twentieth century, an ANN, sometimes called a "connectionist" system (P. M. Todd & Loy, 1991), is a program that attempts to simulate networks of neurons in the animal brain, using virtual/ functional equivalents of biological structures (Zou et al, 2009;Rosa et al, 2020). Their basic function is to learn -usually understood as the capacity to form stable categories from some set of input data -and thus they have been a key architecture in the field of machine learning.…”

Section: Neural Networkmentioning

confidence: 99%

Music in Evolution and Evolution in Music

Jan

2022

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Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives.

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“…One influential model-based approach is the geometric programming algorithm [2]- [4], which models the circuit performance with posynomial approximation. Other modeling strategies also exist, including artificial neural network (ANN) [5]- [7], support vector machine (SVM) [8], and Gaussian process regression (GPR) [9]- [14]. The disadvantage that prevents model-based methods from being widely used is that an accurate performance model is always hard to derive manually or requires a large set of simulation data to approximate.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Batch-Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multiobjective Acquisition Ensemble

Zhang

Yang

Yan

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Bayesian optimization is a promising methodology for analog circuit synthesis. However, the sequential nature of the Bayesian optimization framework significantly limits its ability to fully utilize real-world computational resources. In this paper, we propose an efficient parallelizable Bayesian optimization algorithm via Multi-objective ACquisition function Ensemble (MACE) to further accelerate the optimization procedure. By sampling query points from the Pareto front of the probability of improvement (PI), expected improvement (EI) and lower confidence bound (LCB), we combine the benefits of state-of-theart acquisition functions to achieve a delicate tradeoff between exploration and exploitation for the unconstrained optimization problem. Based on this batch design, we further adjust the algorithm for the constrained optimization problem. By dividing the optimization procedure into two stages and first focusing on finding an initial feasible point, we manage to gain more information about the valid region and can better avoid sampling around the infeasible area. After achieving the first feasible point, we favor the feasible region by adopting a specially designed penalization term to the acquisition function ensemble. The experimental results quantitatively demonstrate that our proposed algorithm can reduce the overall simulation time by up to 74× compared to differential evolution (DE) for the unconstrained optimization problem when the batch size is 15. For the constrained optimization problem, our proposed algorithm can speed up the optimization process by up to 15× compared to the weighted expected improvement based Bayesian optimization (WEIBO) approach, when the batch size is 15.

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Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Cited by 18 publications

References 0 publications

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Music in Evolution and Evolution in Music

An Efficient Batch-Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multiobjective Acquisition Ensemble

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