612 citations · 3.1k across the 120 of their papers we have counts for
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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…
PRIME: Physics-Related Intelligent Mixture of Experts for Transistor Characteristics Prediction
Zhenxing Dou, Yijiao Wang, Tao Zou +4
In recent years, machine learning has been extensively applied to data prediction during process ramp-up, with a particular focus on transistor characteristics for circuit design a…
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,…
TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices
Jianlei Yang, Jiacheng Liao, Fanding Lei +6
Developing deep learning models on tiny devices (e.g. Microcontroller units, MCUs) has attracted much attention in various embedded IoT applications. However, it is challenging to…