Eye Height/Width Prediction From &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$S$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Parameters Using Learning-Based Models

Ambasana, Nikita; Anand, Gowri; Mutnury, Bhyrav; Gope, Dipanjan

doi:10.1109/tcpmt.2016.2552081

Cited by 27 publications

(7 citation statements)

References 21 publications

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“…Furthermore, the authors in [15] demonstrates the superior of ANN by a comprehensive comparison. The authors in [16] enhance the ANN model performance in [15] with feature selection algorithms described in [17,18]. On the other hand, the authors in [19] use the ANN model to model crosstalk in high-speed transmission lines.…”

Section: Introductionmentioning

confidence: 94%

“…This approach provides a fast alternative solution in the time-domain. An extension of this approach in [15] illustrates general applicability with complex numerical examples. Furthermore, the authors in [15] demonstrates the superior of ANN by a comprehensive comparison.…”

Section: Introductionmentioning

confidence: 99%

“…An extension of this approach in [15] illustrates general applicability with complex numerical examples. Furthermore, the authors in [15] demonstrates the superior of ANN by a comprehensive comparison. The authors in [16] enhance the ANN model performance in [15] with feature selection algorithms described in [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems

Kim

2021

Applied Sciences

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In this paper, we propose an efficient design methodology for energy-efficient off-chip interconnect. This approach leverages an artificial neural network (ANN) as a surrogate model that significantly improves design efficiency in the frequency-domain. This model utilizes design specifications as the constraint functions to guarantee the satisfaction of design requirements. Additionally, a specified objective function to select low-loss and low-noise structure is employed to determine the optimal case from a large design space. The proposed design flow can find the optimum design that gives maximum eye height (EH) with the largest allowable transmitter supply voltage (VTX) reduction for minimum power consumption. The proposed approach is applied to the microstrip line and stripline structures with single-ended and differential signals for general applicability. For the microstrip line, the proposed methodology performs at a performance speed with 42.7 and 0.5 s per structure for the data generation and optimization process, respectively. In addition, the optimal microstrip line design achieves a 25%VTX reduction. In stripline structures, it takes 31.9 s for the data generation and 0.6 s for the optimization process per structure when the power efficiency reaches a maximum 30.7% peak to peak VTX decrease.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems

Kim

2021

Applied Sciences

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“…In addition, thorough theoretical research has consolidated eye computation and processing, as well as jitter analysis making them highly reliable, standard tools [36], [37]. Machine learning techniques, such as the ones used in this article, have also been used in the past in relation to eye-diagram analysis [5], [38], [39]. The novelty of the present work comes from the way in which machine learning is used, from the metamodeling strategy itself.…”

Section: Eye Aperture and Data-link Qualitymentioning

confidence: 99%

Stochastic Time-Domain Mapping for Comprehensive Uncertainty Assessment in Eye Diagrams

Telescu,

Trinchero,

Soleimani

et al. 2023

IEEE Trans. Electromagn. Compat.

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The eye diagram is one of the most common tools used for quality assessment in high-speed links. This article proposes a method of predicting the shape of the inner eye for a link subject to uncertainties. The approach relies on machine learning regression and is tested on the very challenging example of flexible link for smart textiles. Several sources of uncertainties are taken into account related to both manufacturing tolerances and physical deformation. The resulting model is fast and accurate. It is also extremely versatile: rather than focusing on a specific metric derived from the eye diagram, its aim is to fully reconstruct the inner eye and enable designers to use it as they see fit. This article investigates the features and convergence of three alternative machine learning algorithms, including the single-output support vector machine regression, together with its least squares variant, and the vector-valued kernel ridge regression. The latter method is arguably the most promising, resulting in an accurate, fast and robust tool enabling a complete parametric stochastic map of the eye.

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“…In addition, optimization procedures widely employ active-learning algorithms such as Bayesian optimization [9], [10], [11]. Alternatively, supervised and unsupervised learning are extensively exploited to enhance design efficiency using machine learning-based surrogate models [19], [20], [21], [22], [26], [28], [27], [16], [17], [18], [23], [24], [25], [12], [13], [14], [15], [29], [30], [31]. To this end, contemporary deep learning techniques with breakthroughs in various applications, such as deep neural networks (DNNs), have emerged as candidates for this surrogate model.…”

Section: Introductionmentioning

confidence: 99%

Length-Dependent Deep Neural Network Based Modeling for High-Speed Channels

Khac Le,

Kim

2024

IEEE Access

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This article presents a length-dependent deep neural network (LD-DNN) based channel modeling methodology to predict the frequency response of high-speed channels. The proposed method significantly enhances the model accuracy and design efficiency while considering the channel length dependence that was neglected in previous modeling approaches. We define the concept of the electrical length to model the length and frequency dependence, then further leverage the activation function to capture the multiple reflection effects to improve accuracy. Additionally, we model the insertion loss resonance induced by crosstalk that can seriously deteriorate signal integrity. As a result, by adopting the proposed model which can predict the S-parameters as a function of length, the need for performing additionally 3D electromagnetic simulations when adjusting the channel length can be eliminated. Various high-speed channel cases are tested to validate the accuracy of the proposed method. The modeling accuracy is less than 4% for different high-speed channel structures with run times of less than 1.4 second per design.

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Eye Height/Width Prediction From <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math> </inline-formula>-Parameters Using Learning-Based Models

Cited by 27 publications

References 21 publications

Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems

Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems

Stochastic Time-Domain Mapping for Comprehensive Uncertainty Assessment in Eye Diagrams

Length-Dependent Deep Neural Network Based Modeling for High-Speed Channels

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