AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference

Author:

Li Min,Li Yu,Tian Ye,Jiang Li,Xu Qiang

Publisher

IEEE

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference;IEEE Transactions on Mobile Computing;2024-10

2. Hybrid SLM and LLM for Edge-Cloud Collaborative Inference;Proceedings of the Workshop on Edge and Mobile Foundation Models;2024-06-03

3. EdgeBoost: Confidence Boosting for Resource Constrained Inference via Selective Offloading;2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT);2024-04-29

4. A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform;Information;2024-03-09

5. Ace-Sniper: Cloud–Edge Collaborative Scheduling Framework With DNN Inference Latency Modeling on Heterogeneous Devices;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-02

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