Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning

Yang, Haoyu; Su, Jing; Zou, Yi; Ma, Yuzhe; Yu, Bei; Young, Evangeline F. Y.

doi:10.1109/tcad.2018.2837078

Cited by 88 publications

(70 citation statements)

References 20 publications

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“…Core corresponds to the central location where a hotspot appears Fig. 9 An example of a neural network for hotspot detection [74] including fuzzy pattern matching, is insufficient to handle never-before-seen hotspot patterns. Recently, machine learning based approaches have demonstrated good generalization capability to recognize unseen hotspot patterns [17,18,45,50,80,82].…”

Section: Hotspot Detectionmentioning

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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Section: Hotspot Detectionmentioning

confidence: 99%

Dealing with Aging and Yield in Scaled Technologies

Alawieh

Hsu

et al. 2020

Dependable Embedded Systems

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“…Similar to CNN's convolutional layer, this operation reduces the size of the original image because n and k are smaller than the size N of the original input image, which can significantly reduce computing time and memory consumption. For details, please refer to (Yang et al) [51].…”

Section: ) Feature Tensor Generation (Tensorization) For Image Classmentioning

confidence: 99%

“…The goal is still to optimize the loss function between the predicted and true values. The author used Tucker decomposition Algorithm 14 Feature Tensor Generation (Yang et al) [51] Input:…”

Section: ) Tensor-based Feature Fusion For Face Recognitionmentioning

confidence: 99%

A Survey on Tensor Techniques and Applications in Machine Learning

Wang

et al. 2019

IEEE Access

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This survey gives a comprehensive overview of tensor techniques and applications in machine learning. Tensor represents higher order statistics. Nowadays, many applications based on machine learning algorithms require a large amount of structured high-dimensional input data. As the set of data increases, the complexity of these algorithms increases exponentially with the increase of vector size. Some scientists found that using tensors instead of the original input vectors can effectively solve these high-dimensional problems. This survey introduces the basic knowledge of tensor, including tensor operations, tensor decomposition, some tensor-based algorithms, and some applications of tensor in machine learning and deep learning for those who are interested in learning tensors. The tensor decomposition is highlighted because it can effectively extract structural features of data and many algorithms and applications are based on tensor decomposition. The organizational framework of this paper is as follows. In part one, we introduce some tensor basic operations, including tensor decomposition. In part two, applications of tensor in machine learning and deep learning, including regression, supervised classification, data preprocessing, and unsupervised classification based on low rank tensor approximation algorithms are introduced detailly. Finally, we briefly discuss urgent challenges, opportunities and prospects for tensor.

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“…. $15.00 https://doi.org/10.1145/3316781.3317876 using has been studied using SVM-Kernels [8] and deep learning [10]. Specifically, for improving the quality of routing estimation at early stages, the most recent works mainly focus on a) forecasting routing congestion map [6] and b) routability prediction [9,11].…”

Section: Introductionmentioning

confidence: 99%

Painting on Placement

Zhang

2019

Proceedings of the 56th Annual Design Automation Conference 2019

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Physical design process commonly consumes hours to days for large designs, and routing is known as the most critical step. Demands for accurate routing quality prediction raise to a new level to accelerate hardware innovation with advanced technology nodes. This work presents an approach that forecasts the density of all routing channels over the entire floorplan, with features collected up to placement, using conditional GANs. Specifically, forecasting the routing congestion is constructed as an image translation (colorization) problem. The proposed approach is applied to a) placement exploration for minimum congestion, b) constrained placement exploration and c) forecasting congestion in real-time during incremental placement, using eight designs targeting a fixed FPGA architecture.

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Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning

Cited by 88 publications

References 20 publications

Dealing with Aging and Yield in Scaled Technologies

Dealing with Aging and Yield in Scaled Technologies

A Survey on Tensor Techniques and Applications in Machine Learning

Painting on Placement

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