2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…