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Found 20 papers, 4 papers with code
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration
Cross-workload design space exploration (DSE) is crucial in CPU architecture design.
Exergy Battery Modeling and P2P Trading Based Optimal Operation of Virtual Energy Station
Virtual energy stations (VESs) work as retailers to provide electricity and natural gas sale services for integrated energy systems (IESs), and guide IESs energy consumption behaviors to tackle the varying market prices via integrated demand response (IDR).
A Profit Sharing Mechanism for Coordinated Power Traffic System
Under this mechanism, the scheduling process of the power traffic system is divided into two stages.
Cost-Effective Label-free Node Classification with LLMs
Graph neural networks (GNNs) have emerged as go-to models for node classification in graph data due to their powerful abilities in fusing graph structures and attributes.
Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need
This approach enhances both the prediction accuracy and interpretability of the performance model.
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
In this work, we focus on leveraging the properties of semantic graphs to enhance HGNN performance.
Characterizing and Understanding HGNN Training on GPUs
Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis.
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
In this study, we introduce Disttack, the first framework of adversarial attacks for distributed GNN training that leverages the characteristics of frequent gradient updates in a distributed system.
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i. e., graph data augmentation and attack.
A Comprehensive Survey on Distributed Training of Graph Neural Networks
This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training.
Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication
This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered.
Towards Joint Electricity and Data Trading: A Scalable Cooperative Game Theoretic Approach
This paper, for the first time, proposes a joint electricity and data trading mechanism based on cooperative game theory.
Rethinking Efficiency and Redundancy in Training Large-scale Graphs
Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.
Simple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.
Ranked #1 on
Heterogeneous Node Classification
on Freebase (Heterogeneous Node Classification)
(Macro-F1 metric)
Characterizing and Understanding Distributed GNN Training on GPUs
Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs.
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Extensive experiments show that our method is universal to mainstream sampling algorithms and helps significantly reduce the training time, especially in large-scale graphs.
Sampling methods for efficient training of graph convolutional networks: A survey
Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations.
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs.
Hardware Architecture
HyGCN: A GCN Accelerator with Hybrid Architecture
In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU.
Distributed, Parallel, and Cluster Computing
