A Survey of Graph Neural Networks for Electronic Design Automation

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

doi:10.1109/mlcad52597.2021.9531070

Cited by 46 publications

(9 citation statements)

References 19 publications

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“…Additionally, many scholars have combed and summarized GNNs from different perspectives (such as methods, applications, etc.). For details, please refer to the review [100][101][102][103][104][105][106][107][108][109][110]. Due to its high degree of freedom, good computability, and high reasoning efficiency, the spatial-based method has been widely concerned and developed.…”

Section: Output Layermentioning

confidence: 99%

Graph-based Knowledge Distillation: A survey and experimental evaluation

Liu¹,

Tongya²,

Zhang³

et al. 2023

Preprint

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Graph data, such as citation networks, social networks, and transportation networks, are prevalent in the real world. Graph neural networks (GNNs) have gained widespread attention for their robust expressiveness and exceptional performance in various graph analysis applications. However, the efficacy of GNNs is heavily reliant on sufficient data labels and complex network models, with the former being challenging to obtain and the latter requiring expensive computational resources. To address the labeled data scarcity and high complexity of GNNs, Knowledge Distillation (KD) has been introduced to enhance existing GNNs. This technique involves transferring the soft-label supervision of the large teacher model to the small student model while maintaining prediction performance. Transferring the KD technique to graph data and graph-based knowledge is a major challenge. This survey offers a comprehensive overview of Graph-based Knowledge Distillation methods, systematically categorizing and summarizing them while discussing their limitations and future directions. This paper first introduces the background of graph and KD. It then provides a comprehensive summary of three types of Graph-based Knowledge Distillation methods, namely Graph-based Knowledge Distillation for deep neural networks (DKD), Graph-based Knowledge Distillation for GNNs (GKD), and Self-Knowledge Distillation based Graph-based Knowledge Distillation (SKD). Each type of method is further divided into knowledge distillation methods based on the output layer, middle layer, and constructed graph. Subsequently, various graph-based knowledge distillation algorithms' ideas are analyzed and compared, concluding with the advantages and disadvantages of each algorithm supported by experimental results. In addition, the applications of graph-based knowledge distillation in computer vision, natural language processing, recommendation systems, and other fields are listed. Finally, the development of graph-based knowledge distillation is summarized and prospectively discussed. We have also released related resources at https://github.com/liujing1023/Graph-based-Knowledge-Distillation.

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Section: Output Layermentioning

confidence: 99%

Graph-based Knowledge Distillation: A survey and experimental evaluation

Liu¹,

Tongya²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, there was little focus on challenges and future directions. In [29], a comprehensive review of Graphical Neural Networks (GNNs) for EDA is presented highlighting the areas of logic synthesis, physical design and verification. As graphs are intuitive way of representing circuits, netlists and layout, GNN can be easily fit in EDA to solve combinatorial optimization problems at various levels and improve the QoR (Quality of Results) [30].…”

Section: Reviews Ic Testingmentioning

confidence: 99%

AI/ML Algorithms and Applications in VLSI Design and Technology

Amuru¹,

Vudumula²,

K.³

et al. 2022

Preprint

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An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and dataintensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.

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“…4 for cross-learning between radar and camera information [23]. The architecture is composed of a GNN [24], specifically DGCNN [12], followed by LSTM layers for recognition of gesture sequences.…”

Section: Architecture and Learningmentioning

confidence: 99%

Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition

Hazra¹,

Feng²,

Kiprit³

et al. 2022

Preprint

Self Cite

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Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic properties, such as their ability to work in low illumination, harsh weather conditions, and being low-cost and compact, making them highly preferable for a gesture recognition solution. However, most literature work focuses on solutions with a limited range that is lower than a meter. We propose a novel architecture for a long-range (1m -2m) gesture recognition solution that leverages a point cloud-based cross-learning approach from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning better representations while suppressing noise. We use a variant of Dynamic Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships between the points at a local and global level and to model the temporal dynamics a Bi-LSTM network is employed. In the experimental results section, we demonstrate our model's overall accuracy of 98.4% for five gestures and its generalization capability.

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A Survey of Graph Neural Networks for Electronic Design Automation

Cited by 46 publications

References 19 publications

Graph-based Knowledge Distillation: A survey and experimental evaluation

Graph-based Knowledge Distillation: A survey and experimental evaluation

AI/ML Algorithms and Applications in VLSI Design and Technology

Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition

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