A Comprehensive Survey on Electronic Design Automation and Graph Neural Networks: Theory and Applications

Lopera, Daniela Sánchez; Servadei, Lorenzo; Kiprit, Gamze Naz; Wille, Robert; Ecker, Wolfgang

doi:10.1145/3543853

Cited by 12 publications

(2 citation statements)

References 36 publications

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“…Undoubtedly, the potential for using autonomous vehicles, electric vehicles, and autonomous electric vehicles to enhance the sustainability of urban transportation is immense. Autonomous vehicles drive more conservatively; therefore, autonomous vehicles will significantly reduce gasoline and energy consumption during driving compared to traditional manually driven cars [130,131]. However, this research does not consider autonomous vehicles and autonomous electric vehicles factors.…”

Section: Limitationsmentioning

confidence: 99%

Consumers’ Purchase Intention toward Electric Vehicles from the Perspective of Perceived Green Value: An Empirical Survey from China

Zhao,

Furuoka,

Rasiah

et al. 2024

WEVJ

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This study aims to expand the current understanding of the antecedents and consequences of green purchase intention within the context of perceived green value (PGV). This study uniquely combines PLS-SEM and NCA to provide a comprehensive analysis of the factors influencing green purchase intention, offering a novel methodological contribution to the field. By examining the roles of influence and interaction as key precursors to perceived responsibility and self-efficacy, the research adds new dimensions to understanding how these factors shape green purchase intentions. Additionally, we explore how PER and PSE impact green purchase intention (GPI), primarily through their mediating effects. The survey data collected from consumers reveal that PGV positively influences GPI. Furthermore, both PER and PSE exhibit significant chain mediation effects, with the mediation effect of perceived environmental responsibility being particularly strong. This study provides actionable insights for policymakers and electric vehicle manufacturers, emphasizing the importance of green education, certification systems, and the promotion of environmental and economic attributes to foster green purchasing behavior. The findings suggest that government efforts should focus on intensifying green education and promoting the establishment of certification and evaluation systems for electric vehicle products. Meanwhile, electric vehicle manufacturers should highlight the environmental and economic benefits of their products to stimulate green purchasing behavior.

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Section: Limitationsmentioning

confidence: 99%

Consumers’ Purchase Intention toward Electric Vehicles from the Perspective of Perceived Green Value: An Empirical Survey from China

Zhao,

Furuoka,

Rasiah

et al. 2024

WEVJ

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show abstract

“…While EDA tools offer scalability, reliability, and time-to-market advantages, they are often computationally demanding and do not always guarantee optimal solutions. To address these limitations, researchers have turned to GNNs as a promising new approach for solving EDA problems directly using graph structures for circuits, intermediate Register Transfer Levels, and netlists [120], [121], [122].…”

Section: B Academic Applicationsmentioning

confidence: 99%

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Li,

Xue,

Zargari

et al. 2023

IEEE Access

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Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graphstructured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs' general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNNbased case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs' unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs' latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

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Graph pooling in graph neural networks: methods and their applications in omics studies

Wang,

Hou,

Sheng

et al. 2024

Artif Intell Rev

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A Comprehensive Survey on Electronic Design Automation and Graph Neural Networks: Theory and Applications

Cited by 12 publications

References 36 publications

Consumers’ Purchase Intention toward Electric Vehicles from the Perspective of Perceived Green Value: An Empirical Survey from China

Consumers’ Purchase Intention toward Electric Vehicles from the Perspective of Perceived Green Value: An Empirical Survey from China

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Graph pooling in graph neural networks: methods and their applications in omics studies

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