Automated Design of Analog and High-frequency Circuits

Liu, Bo; Gielen, Georges; Fernández, Francisco V.

doi:10.1007/978-3-642-39162-0

Cited by 40 publications

(40 citation statements)

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“…Following the same method as in [24], SMDN is revised to handle constrained optimization problems by using a revised tournament selection-based method to rank the generated candidate solutions considering their level of constraint satisfaction. SBDE uses a constraint satisfaction considering tournament selection method [25] to replace the selection operator in DE, which is widely used in many electronic design optimization problems [3]. The parameter setting of NDPAD and SBDE follows [24].…”

Section: Network On Chip (Noc) Design Optimizationmentioning

confidence: 99%

“…Instead, we focus on discrete numerical variables, which are sometimes unavoidable in product design and manufacturing. For example, [3], [4] show that most discrete variables in electronic circuit and system design (e.g., number of fingers of transistors, design grids) are discrete numerical variables and other categories of discrete variables are often not involved. In this paper, we try to obtain combined advantages on efficiency and solution quality for complex EDOD.…”

Section: Introductionmentioning

confidence: 99%

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A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

Liu

Sun

Zhang

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Real-world computationally expensive design optimization problems with discrete variables pose challenges to surrogate-based optimization methods in terms of both efficiency and search ability. In this paper, a new method is introduced, called surrogate model-aware differential evolution with neighbourhood exploration, which has two phases. The first phase adopts a surrogate-based optimization method based on efficient surrogate model-aware search framework, the goal of which is to reach at least the neighbourhood of the global optimum. In the second phase, a neighbourhood exploration method for discrete variables is developed and collaborates with the first phase to further improve the obtained solutions. Empirical studies on various benchmark problems and a realworld network-on-chip design optimization problem show the combined advantages in terms of efficiency and search ability: when only a very limited number of exact evaluations are allowed, the proposed method is not slower than one of the most efficient methods for the targeted problem; when more evaluations are allowed, the proposed method can obtain results with comparable quality compared to standard differential evolution, but it requires only 1% to 30% of exact function evaluations.

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Section: Network On Chip (Noc) Design Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

Liu

Sun

Zhang

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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“…The evolutionary computation algorithm imitates the biological mechanisms of evolution to approximate the global extremum problem [15]. The main feature of the evolutionary algorithm (EA) is the use of individual populations that are processed by a set of operators (crossover, mutation, selection) and are evaluated using the fitness function.…”

Section: Evolutional Algorithm and Surrogate Modellingmentioning

confidence: 99%

“…Other recent research papers [13][14] describe computational algorithms for non-linear deformation. Optimization plays a key role in the design of actuators [15][16][17][18], however, most MEMS design optimization (exploration) methods depend either on analytical / behavioural models or on time consuming numerical simulations. Surrogate modelling techniques have been introduced to integrate generality and efficiency [19].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of a discrete-action thermo-bimetallic actuator with a tongue

Nikolaeva

McMillan

Gavriushin

2016

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. The selection of suitable parameters, by experimental or intuitive processes for snap-through actuation of a bimetallic actuator at a prescribed temperature is an extremely time-consuming task. This paper describes a new methodology for the optimization of a discrete action thermo-bimetallic actuator with a tongue. This methodology makes it possible to solve the optimization task with higher efficiency. The requirement is to find optimal parameters values so that the actuator will make a snap-through at a given temperature. The constrained optimization task was performed using an evolutional algorithm and surrogate modelling and this was coded in Matlab. Functional relationships between the criteria and parameters were not set explicitly, but they were calculated using finite element method, each simulation of which was performed in Abaqus.

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“…Among the various approaches presented in literature [1], [2], the simulation-based approaches present the least burden on the circuit designers since the tool finds the optimum parameters by iteratively running the circuit simulations that the designers would have to run themselves. However, the downside is long execution time, since typically hundreds to even thousands of simulations may be required before finding the optimal design [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Efficient Global Optimization of Analog Circuits Using Predictive Response Surface Models on Discretized Design Space

Lee

Kim

2016

IEEE Des. Test

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Abstract─ An efficient global optimization method is presented. Typically, simulation-based optimizers have long execution time due to the large number of simulations required during the optimization process.This paper tries to reduce the simulation number with two existing techniques: first, discretizing the continuous design space with respect to the variability-induced correlation, making total number of design candidate to be finite; second, performing simulation for only the best design candidates determined based on response surface models (RSMs) on stochastic circuit performance. For more efficient searching, extrapolative RSM and a heuristic for finding best candidate are also introduced. The RSM-based searching is demonstrated to significantly enhance the scalability of discrete, gradient-based searching. Also, it shows 2x~3x reduction in the number of simulations for the optimization of a standard two-stage operational amplifier, a voltage-controlled LC oscillator and a differential voltage-controlled ring oscillator compared to the genetic algorithm.

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Automated Design of Analog and High-frequency Circuits

Cited by 40 publications

References 0 publications

A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

Synthesis of a discrete-action thermo-bimetallic actuator with a tongue

Efficient Global Optimization of Analog Circuits Using Predictive Response Surface Models on Discretized Design Space

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