Enabling High-Dimensional Bayesian Optimization for Efficient Failure Detection of Analog and Mixed-Signal Circuits

Hu, Hanbin; Huang, Jianhua Z.

doi:10.1145/3316781.3317818

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…To ensure a fair comparison and reduce implementation bias, we implement the Gaussian process regression model with GPyOpt [28] library for all test algorithms. For REMBOpBO, we follow the experimental setting of [29] and optimize the acquisition function with NLopt [42] library. For the rest of the algorithms, we implement the inner optimization procedure with Scipy [43] library.…”

Section: Resultsmentioning

confidence: 99%

“…( 2) LP-LCB [28], an optimistic policy that introduces a local repulsive term to reduce redundantly sampling around the same area. ( 3) REMBOpBO [29], an efficient Bayesian optimization framework that handles the high-dimensional problems by projecting the design variables into a high effective subspace. ( 4) LinEasyBO, which is our proposed asynchronous algorithm in batch mode.…”

Section: Resultsmentioning

confidence: 99%

“…We also leverage the carefully designed batch Bayesian optimization framework to further increase sampling efficiency. Experimental results quantitatively demonstrate that our proposed algorithm can accelerate the optimization procedure by up to 9× and 38× compared to LP-EI [28] and REMBOpBO [29] respectively while obtaining the same optimization results when the batch size is 15.…”

Section: Introductionmentioning

confidence: 94%

See 2 more Smart Citations

Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network

Zhang

Lyu

Yang

et al. 2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis. In the traditional Gaussian process regression model, the kernel functions are defined explicitly. The computational complexity of training is O(N 3 ), and the computation complexity of prediction is O(N 2 ), where N is the number of training data. Gaussian process model can also be derived from a weight space view, where the original data are mapped to feature space, and the kernel function is defined as the inner product of nonlinear features. In this paper, we propose a Bayesian optimization approach for analog circuit synthesis using neural network. We use deep neural network to extract good feature representations, and then define Gaussian process using the extracted features. Model averaging method is applied to improve the quality of uncertainty prediction. Compared to Gaussian process model with explicitly defined kernel functions, the neuralnetwork-based Gaussian process model can automatically learn a kernel function from data, which makes it possible to provide more accurate predictions and thus accelerate the follow-up optimization procedure. Also, the neural-network-based model has O(N ) training time and constant prediction time. The efficiency of the proposed method has been verified by two realworld analog circuits.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

See 1 more Smart Citation

Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network

Zhang

Lyu

Yang

et al. 2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

View full text Add to dashboard Cite

show abstract

“…Moreover, with the increase in AMS circuits complexity, increasing nonlinearity stands out as major factor limiting the capabilities of performance modeling and optimization. Hence, performance optimization techniques relying on nonparametric surrogate models and Bayesian optimization frameworks have been recently proposed [31,83]. These surrogate models are typically Gaussian Processes, and Bayesian optimization is used to find optimal values given a black-box function.…”

Section: Performance Optimizationmentioning

confidence: 99%

“…The new data collected at each step augments the training dataset to retrain a probabilistic surrogate model that approximates the black-box function. Such iterative sampling scheme contributes directly to the accuracy of the surrogate model and guides the iterative global optimization process [31,83].…”

Section: Performance Optimizationmentioning

confidence: 99%

Dealing with Aging and Yield in Scaled Technologies

Alawieh

Hsu

et al. 2020

Dependable Embedded Systems

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This chapter reviews recent practices of tackling aging and yield issues in VLSI design related to shrinking technology processes. Different fundamental effects such as device aging, interconnect electromigration, and process variations are investigated with the state-of-the-art techniques for modeling and optimization. The presented techniques vary from analytical approaches to machine learning, and often require cross-layer information feedback for robust design cycles.

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