Y
Yifan Wang
Researcher at Chinese Academy of Sciences
Publications - 18
Citations - 804
Yifan Wang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Edge computing & Cloud computing. The author has an hindex of 11, co-authored 18 publications receiving 467 citations. Previous affiliations of Yifan Wang include Wayne State University.
Papers
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Journal ArticleDOI
Edge Computing for Autonomous Driving: Opportunities and Challenges
TL;DR: In this paper, the authors review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges, including providing enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles.
Proceedings ArticleDOI
OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs
TL;DR: An Open Vehicular Data Analytics Platform (OpenVDAP) for CAVs is proposed, which is a full-stack edge based platform including an on-board computing/communication unit, an isolation-supported and security & privacy-preserved vehicle operation system, an edge-aware application library, as well as an optimal workload of?oading and scheduling strategy, allowing CAVs to dynamically detect each service's status, computation overhead and the optimal of?:oading destination.
Proceedings ArticleDOI
OpenEI: An Open Framework for Edge Intelligence
TL;DR: An Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability and analyzes four fundamental EI techniques used to build OpenEI and identifies several open problems based on potential research directions.
pCAMP: Performance Comparison of Machine Learning Packages on the Edges
TL;DR: In this article, a performance comparison of several state-of-the-art machine learning packages on the edge devices is made, including TensorFlow, Caffe2, MXNet, PyTorch, and Tensorflow Lite.
Posted Content
pCAMP: Performance Comparison of Machine Learning Packages on the Edges
TL;DR: In this paper, a performance comparison of several state-of-the-art machine learning packages on the edge devices is made, including TensorFlow, Caffe2, MXNet, PyTorch, and Tensorflow Lite.