2 citations · 4 across the 9 of their papers we have counts for
11 papers
HAPMoE: Heterogeneity-Aware Automatic Parallelism Planning for Mixture-of-Experts Models Training
Mengyuan Fan, Peizhuang Cong, Zixiao Huang +7
As model sizes continue to scale, distributed training has become inevitable. Automatic parallelization techniques can derive efficient training parallelism strategies at low cost…
MegaGraph: Towards Efficient Training of Large-Scale Graph Transformers with Automated Hybrid Parallelism
Tong Qiao, Ao Zhou, Yingjie Qi +2
Graph Transformers (GTs) offer superior representation capabilities by overcoming the depth limitations and over-smoothing issues of traditional Graph Neural Networks (GNNs). Howev…
GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search
Ao Zhou, Jianlei Yang, Tong Qiao +4
Graph Neural Networks (GNNs) have emerged as the state-of-the-art graph learning method. However, achieving efficient GNN inference on edge devices poses significant challenges, li…
ACE-GNN: Adaptive GNN Co-Inference with System-Aware Scheduling in Dynamic Edge Environments
Ao Zhou, Jianlei Yang, Tong Qiao +5
The device-edge co-inference paradigm effectively bridges the gap between the high resource demands of Graph Neural Networks (GNNs) and limited device resources, making it a promis…
Towards Affordable, Adaptive and Automatic GNN Training on CPU-GPU Heterogeneous Platforms
Tong Qiao, Ao Zhou, Yingjie Qi +4
Graph Neural Networks (GNNs) have been widely adopted due to their strong performance. However, GNN training often relies on expensive, high-performance computing platforms, limiti…
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices
Ao Zhou, Jianlei Yang, Yingjie Qi +5
Graph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Never…
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
Tong Qiao, Jianlei Yang, Yingjie Qi +5
Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for var…
Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
Ao Zhou, Jianlei Yang, Tong Qiao +4
The key to device-edge co-inference paradigm is to partition models into computation-friendly and computation-intensive parts across the device and the edge, respectively. However,…
Architectural Implications of GNN Aggregation Programming Abstractions
Yingjie Qi, Jianlei Yang, Ao Zhou +2
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN comp…
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms
Ao Zhou, Jianlei Yang, Yingjie Qi +4
Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model de…
Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
Ao Zhou, Jianlei Yang, Yeqi Gao +7
Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-Of-Memory (OOM) p…