Fast Design Optimization Through Simple Kriging Metamodeling: A Sense Amplifier Case Study

Okobiah, Oghenekarho; Mohanty, Saraju P.; Kougianos, Elias

doi:10.1109/tvlsi.2013.2256436

Cited by 27 publications

(7 citation statements)

References 11 publications

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“…Early work in this area is based mostly on offline training: the model is fitted once on many available datapoints and then used with very few updates during the optimization process. For example [31,32] combine GPs (only making use of the predicted values, but not of the uncertainty information) with multiobjective search algorithms for circuit sizing.…”

Section: Surrogate Modelsmentioning

confidence: 99%

Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference

Vişan¹,

Pascu²,

Stănescu³

et al. 2022

Preprint

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With the ever increasing complexity of specifications, manual sizing for analog circuits recently became very challenging. Especially for innovative, large-scale circuits designs, with tens of design variables, operating conditions and conflicting objectives to be optimized, design engineers spend many weeks, running time-consuming simulations, in their attempt at finding the right configuration.Recent years brought machine learning and optimization techniques to the field of analog circuits design, with evolutionary algorithms and bayesian models showing good results for circuit sizing.In this context, we introduce a design optimization method based on Generalized Differential Evolution 3 (GDE3) and Gaussian Processes (GPs). The proposed method is able to perform sizing for complex circuits with a large number of design variables and many conflicting objectives to be optimized. While state-of-the-art methods reduce multi-objective problems to single-objective optimization and potentially induce a priori bias, we search directly over the multiobjective space using Pareto dominance and ensure that diverse solutions are provided to the designers to choose from. To the best of our knowledge, the proposed method is the first to specifically address the diversity of the solutions, while also focusing on minimizing the number of simulations required to reach feasible configurations. We evaluate the introduced method on two voltage regulators showing different levels of complexity and we highlight that the proposed

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Section: Surrogate Modelsmentioning

confidence: 99%

Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference

Vişan¹,

Pascu²,

Stănescu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…After learning the system behavior, the model is able to predict how the system will respond to any given input, and predict its output. Surrogate models have been used in the literature, for instance, to model circuit performances [25] or device variability [26]. The work presented here uses an extremely accurate surrogate model that has less than 1% error when compared with EM simulations [20].…”

Section: Accurate Inductor Modelingmentioning

confidence: 99%

A Multilevel Bottom-Up Optimization Methodology for the Automated Synthesis of RF Systems

Passos

Roca

Sieiro

et al. 2020

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

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In recent years there has been a growing interest in electronic design automation methodologies for the optimizationbased design of radiofrequency circuits and systems. While for simple circuits several successful methodologies have been proposed, these very same methodologies exhibit significant deficiencies when the complexity of the circuit is increased. The majority of the published methodologies that can tackle radiofrequency systems are either based on high-level system specification tools or use models to estimate the system performances. Hence, such approaches do not usually provide the desired accuracy for RF systems. In this work, a methodology based on hierarchical multilevel bottom-up design approaches is presented, where multi-objective optimization algorithms are used to design an entire radiofrequency system from the passive component level up to the system level. Furthermore, each level of the hierarchy is simulated with the highest accuracy possible: electromagnetic simulation accuracy at device-level and electrical simulations at circuit/system-level.

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“…For this reason, a different DOE using the Latin Hypercube was implemented. This DOE is commonly combined with Kriging [26,32] because the distribution of data is appropriate for Kriging and consequently the sampling needed to define the metamodel can be reduced. It divides the domain search into different equal spaces and allocates randomly sampling points so that there is only one sampling point in each row and column of the search domain.…”

Section: Design Of Experiments (Doe)mentioning

confidence: 99%

Lightweight parametric design optimization for 4D printed parts

Paz

Pei

Monzón

et al. 2017

ICA

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4D printing is a technology that combines the capabilities of 3D printing with materials that can transform its geometry after being produced (e.g. Shape Memory Polymers). These advanced materials allow shape change by applying different stimulus such as heating. A 4D printed part will usually have 2 different shapes: a programmed shape (before the stimulus is applied), and the original shape (which is recovered once the stimulus has been applied). Lightweight parametric optimization techniques are used to find the best combination of design variables to reduce weight and lower manufacturing costs. However, current optimization techniques available in commercial 3D CAD software are not prepared for optimization of multiple shapes. The fundamental research question is how to optimize a design that will have different shapes with different boundary conditions and requirements. This paper presents a new lightweight parametric optimization method to solve this limitation. The method combines the Latin Hypercube design of experiments, Kriging metamodel and specifically designed genetic algorithms. The optimization strategy was implemented and automated using a CAD software. This method recognizes both shapes of the part as a single design and allows the lightweight parametric optimization to retain the minimum mechanical properties for both shapes.

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Fast Design Optimization Through Simple Kriging Metamodeling: A Sense Amplifier Case Study

Cited by 27 publications

References 11 publications

Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference

Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference

A Multilevel Bottom-Up Optimization Methodology for the Automated Synthesis of RF Systems

Lightweight parametric design optimization for 4D printed parts

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