Reproduce “Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence” on OpenResearch
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About this paper
Shuiguang Deng, Hailiang Zhao, Weijia Fang, Jianwei Yin, Schahram Dustdar, Albert Y. Zomaya
Deng et al. introduce a foundational framework for Edge Intelligence, delineating the field into two branches: "AI for edge" (applying AI to optimize edge computing) and "AI on edge" (deploying AI models on edge devices). The work establishes a comprehensive research road-map and highlights key advancements and challenges across both domains.
Key insights
- Edge Intelligence benefits from a dual perspective: AI optimizing edge computing (AI for edge) and AI models running on edge devices (AI on edge).
- Quality of Experience (QoE), defined by factors like performance, cost, privacy, and efficiency, provides a comprehensive metric for evaluating Edge Intelligence solutions.
- The interactive relationship between Edge Computing and AI involves mutual benefits, where AI optimizes edge operations and edge provides new platforms for AI applications.
Summary by alphaXiv.
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