FactionFormer: Context-Driven Collaborative Vision Transformer Models for Edge Intelligence
Author:
Affiliation:
1. University of Missouri-Kansas City,Missouri,USA
2. NEC Laboratories America,New Jersey,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10207555/10207557/10207673.pdf?arnumber=10207673
Reference10 articles.
1. PipeEdge: Pipeline Parallelism for Large-Scale Model Inference on Heterogeneous Edge Devices
2. AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
3. Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
4. Efficientformer: Vision transformers at mobilenet speed;li;Adv in NeurIPS,2022
5. Edgevits: Competing light-weight cnns on mobile devices with vision transformers;pan;ECCV,2022
Cited by 2 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. Context-Driven Device-Edge Collaborative Vision Transformer Models for Edge Ai;2024
2. Context-Driven Device-Edge Collaborative Vision Transformer Models for Edge Ai;2023
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