A Hybrid Methodology for Jitter and Eye Estimation in High-Speed Serial Channels Using Polynomial Chaos Surrogate Models

Dolatsara, Majid Ahadi; Hejase, Jose A.; Becker, W.D.; Swaminathan, Madhavan

doi:10.1109/access.2019.2908799

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…On the other hand, it was shown that using a TP truncation of order p = 1 for both the numerator and denominator of a rational model yields an exact model for lumped circuits [22]. Because of the PCA transformation, however, the exactness no longer holds for the PCA coefficients in (15), even for lumped systems. Therefore, whereas in [21] the above property motivated, by extension, the choice of a TP truncation also for distributed systems, although generally with p > 1, here we will also consider the more compact TD truncation for the rational model ( 15).…”

Section: Pce Truncationmentioning

confidence: 99%

Fast Stochastic Surrogate Modeling via Rational Polynomial Chaos Expansions and Principal Component Analysis

Manfredi

Grivet-Talocia

2021

IEEE Access

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This paper introduces a fast stochastic surrogate modeling technique for the frequency-domain responses of linear and passive electrical and electromagnetic systems based on polynomial chaos expansion (PCE) and principal component analysis (PCA). A rational PCE model provides high accuracy, whereas the PCA allows compressing the model, leading to a reduced number of coefficients to estimate and thereby improving the overall training efficiency. Furthermore, the PCA compression is shown to provide additional accuracy improvements thanks to its intrinsic regularization properties. The effectiveness of the proposed method is illustrated by means of several application examples.

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Section: Pce Truncationmentioning

confidence: 99%

Fast Stochastic Surrogate Modeling via Rational Polynomial Chaos Expansions and Principal Component Analysis

Manfredi

Grivet-Talocia

2021

IEEE Access

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“…The burgeoning field of machine learning led to the rapid availability of new surrogate modeling paradigms in the integrated circuit design and signal integrity analysis [3], [4], [5], including for example support-vector machines [6], [7], [8], [9], Gaussian processes [10], [11], [12], neural networks [13], [14], and reinforcement learning [15], [16]. Alternatively, the method of polynomial chaos expansion also became widely popular for UQ [17], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Conservative Gaussian Process Models for Uncertainty Quantification and Bayesian Optimization in Signal Integrity Applications

Manfredi

2024

IEEE Trans. Compon., Packag. Manufact. Technol.

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Surrogate modeling is being increasingly adopted in signal and power integrity analysis to assist design exploration, optimization, and uncertainty quantification tasks. In this scenario, machine learning methods are attracting an ever-growing interest over alternative and well-consolidated techniques due to their data-driven nature. However, an open issue is to properly assess the trustworthiness of predictions when generalizing beyond training data. Among various machine learning tools, Gaussian process regression (GPR) has the notable feature of providing an estimate of the prediction uncertainty (or confidence) due to the lack of data. Nevertheless, the uncertainty introduced by the estimation of the Gaussian process parameters, which is part of the training process, is typically not accounted for. In this paper, we introduce improved GPR formulations that take into account the additional uncertainty related to the estimation of (some of) the Gaussian process parameters, thereby providing a more accurate estimate of the actual prediction confidence. Furthermore, the advocated framework is extended to uncertainty quantification and Bayesian optimization settings. The technique is applied to two test cases concerning the analysis of crosstalk in a transmission line network and of the frequencydomain response of a microstrip line with a ground plane discontinuity.

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“…Recently, more modern transient simulation and eye diagram modeling techniques have been pursued, owing to the advancements in surrogate modeling and machine learning techniques. For example, [8] uses a short transient simulation to train a polynomial chaos surrogate model and the surrogate model is then used to estimate the jitter and eye diagram of the output signal, while [9] uses Bayesian optimization to perform a worst-case analysis of the eye diagram. In addition, neural network based techniques have also been developed such as in the application of multilayer perceptron (MLP) neural network for the modeling of the eye diagram [10]- [13], jitter [14], channel equalization [15], and physical parameters such as resistance, conductance, inductance, and capacitance [16].…”

Section: Introductionmentioning

confidence: 99%

Transient Simulations of High-Speed Channels Using CNN-LSTM With an Adaptive Successive Halving Algorithm for Automated Hyperparameter Optimizations

Goay

Ahmad

Goh³

2021

IEEE Access

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Transient simulations of high-speed channels can be very time intensive. Recurrent neural network (RNN) based methods can be used to speed up the process by training a RNN model on a relatively short bit sequence, and then using a multi-steps rolling forecast method to predict subsequent bits. However, the performance of the RNN model is highly affected by its hyperparameters. We propose an algorithm named adaptive successive halving automated hyperparameter optimization (ASH-HPO) which combines successive halving, Bayesian optimization (BO), and progressive sampling to tune the hyperparameters of the RNN models. Modifications are proposed to the successive halving and progressive sampling algorithms for better efficiency on time series data. The ASH-HPO algorithm trains on smaller dataset subsets initially, then expands the training dataset progressively and adaptively adds or removes models along the process. In this paper, we use the ASH-HPO algorithm to optimize the hyperparameters of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and CNN-LSTM networks. We demonstrate the effectiveness of the ASH-HPO algorithm using a PCIe Gen 2 channel, a PCIe Gen 5 channel, and a PAM4 differential channel. We also investigate the effects of several settings and tunable variables of the ASH-HPO algorithm on its convergence speed. As a benchmark, we compared the ASH-HPO algorithm to three state-of-the-art HPO methods: BO, successive halving, and hyperband. The results show that the ASH-HPO algorithm converges faster than the other HPO methods on transient simulation problems.INDEX TERMS Automated hyperparameter optimization, convolutional neural network (CNN), highspeed channel, long short-term memory (LSTM) network, progressive sampling, transient simulation.

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A Hybrid Methodology for Jitter and Eye Estimation in High-Speed Serial Channels Using Polynomial Chaos Surrogate Models

Cited by 10 publications

References 22 publications

Fast Stochastic Surrogate Modeling via Rational Polynomial Chaos Expansions and Principal Component Analysis

Fast Stochastic Surrogate Modeling via Rational Polynomial Chaos Expansions and Principal Component Analysis

Conservative Gaussian Process Models for Uncertainty Quantification and Bayesian Optimization in Signal Integrity Applications

Transient Simulations of High-Speed Channels Using CNN-LSTM With an Adaptive Successive Halving Algorithm for Automated Hyperparameter Optimizations

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