10 papers
AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning
Lixian Jing, Jianpeng Qi, Junyu Dong +1
As deep neural networks (DNNs) are increasingly deployed on edge devices, optimizing models for constrained computational resources is critical. Existing auto-pruning methods face…
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion
Xiang Li, Jianpeng Qi, Haobing Liu +6
Graph Neural Networks (GNNs) have demonstrated impressive performance across diverse graph-based tasks by leveraging message passing to capture complex node relationships. However,…
Decision-Aware Semantic State Synchronization in Compute-First Networking
Jianpeng Qi, Chao Liu, Chengrui Wang +3
In Compute-First Networking (CFN), an Access Point (AP) makes task offloading decisions based on resource state information reported by a Service Node (SN). A fundamental challenge…
Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs
Zhiyuan Zheng, Jianpeng Qi, Jiantao Li +3
Understanding the dynamic transition of motifs in temporal graphs is essential for revealing how graph structures evolve over time, identifying critical patterns, and predicting fu…
A Survey on Open-Source Edge Computing Simulators and Emulators: The Computing and Networking Convergence Perspective
Jianpeng Qi, Chao Liu, Xiao Zhang +4
Edge computing, with its low latency, dynamic scalability, and location awareness, along with the convergence of computing and communication paradigms, has been successfully applie…
Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI
Jianpeng Qi, Chao Liu, Chengxiang Xu +3
Timely and efficient dissemination of service information is critical in compute-first networking systems, where user requests arrive dynamically and computing resources are constr…