Machine-Learning-Driven Matrix Ordering for Power Grid Analysis

Cui, Guixiang; Yu, Wenjian; Li, Xin; Zeng, Zhiyu; Gu, Ben

doi:10.23919/date.2019.8715086

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…Set-to-sequence ML models also play an important role in data-intensive 3D point cloud processing (Qi et al, 2017) and meta-learning (Huang et al, 2018b). Set-input and set-ordering problems themselves are prominent in a wide array of applications ranging from power grid optimization (Cui et al, 2019), where solving them led to power usage savings of up to 30%, through anomaly detection (Jung et al, 2015) to measurements of contaminated galaxy clusters (Ntampaka et al, 2016).…”

Section: What Is Set-to-sequence?mentioning

confidence: 99%

Set-to-Sequence Methods in Machine Learning: A Review

Jurewicz¹,

Derczynski

2021

jair

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Machine learning on sets towards sequential output is an important and ubiquitous task, with applications ranging from language modelling and meta-learning to multi-agent strategy games and power grid optimization. Combining elements of representation learning and structured prediction, its two primary challenges include obtaining a meaningful, permutation invariant set representation and subsequently utilizing this representation to output a complex target permutation. This paper provides a comprehensive introduction to the _eld as well as an overview of important machine learning methods tackling both of these key challenges, with a detailed qualitative comparison of selected model architectures.

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Section: What Is Set-to-sequence?mentioning

confidence: 99%

Set-to-Sequence Methods in Machine Learning: A Review

Jurewicz¹,

Derczynski

2021

jair

View full text Add to dashboard Cite

show abstract

“…Set-to-sequence ML models also play an important role in data-intensive 3D point cloud processing (Qi, Su, Mo, & Guibas, 2017) and meta-learning (Huang, Wang, Singh, Yih, & He, 2018b). Set-input and set-ordering problems themselves are prominent in a wide array of applications ranging from power grid optimization (Cui, Yu, Li, Zeng, & Gu, 2019), where solving them led to power usage savings of up to 30%, through anomaly detection (Jung, Berges, Garrett, & Poczos, 2015) to measurements of contaminated galaxy clusters (Ntampaka, Trac, Sutherland, Fromenteau, Poczos, & Schneider, 2016).…”

Section: Why Does Set-to-sequence Matter?mentioning

confidence: 99%

Set-to-Sequence Methods in Machine Learning: a Review

Jurewicz,

Strømberg-Derczynski

2021

Preprint

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Machine learning on sets towards sequential output is an important and ubiquitous task, with applications ranging from language modelling and meta-learning to multi-agent strategy games and power grid optimization. Combining elements of representation learning and structured prediction, its two primary challenges include obtaining a meaningful, permutation invariant set representation and subsequently utilizing this representation to output a complex target permutation. This paper provides a comprehensive introduction to the field as well as an overview of important machine learning methods tackling both of these key challenges, with a detailed qualitative comparison of selected model architectures.

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“…However, few closely related works are discussed here. Cui et al [5] proposed a machine learning technique for power grid analysis by doing matrixreordering. Fang et al [6] proposed machine learning-based dynamic IR drop prediction.…”

Section: B Related Work 1) Conventional Approaches In Powermentioning

confidence: 99%

PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning

Dey,

Nandi,

Trivedi

2020

Preprint

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With the increase in the complexity of chip designs, VLSI physical design has become a time-consuming task, which is an iterative design process. Power planning is that part of the floorplanning in VLSI physical design where power grid networks are designed in order to provide adequate power to all the underlying functional blocks. Power planning also requires multiple iterative steps to create the power grid network while satisfying the allowed worst-case IR drop and Electromigration (EM) margin. For the first time, this paper introduces Deep learning (DL)-based framework to approximately predict the initial design of the power grid network, considering different reliability constraints. The proposed framework reduces many iterative design steps and speeds up the total design cycle. Neural Network-based multi-target regression technique is used to create the DL model. Feature extraction is done, and training dataset is generated from the floorplans of some of the power grid designs extracted from IBM processor. The DL model is trained using the generated dataset. The proposed DL-based framework is validated using a new set of power grid specifications (obtained by perturbing the designs used in the training phase). The results show that the predicted power grid design is closer to the original design with minimal prediction error (∼2%). The proposed DLbased approach also improves the design cycle time with a speedup of ∼6× for standard power grid benchmarks.

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Machine-Learning-Driven Matrix Ordering for Power Grid Analysis

Cited by 12 publications

References 8 publications

Set-to-Sequence Methods in Machine Learning: A Review

Set-to-Sequence Methods in Machine Learning: A Review

Set-to-Sequence Methods in Machine Learning: a Review

PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning

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