系统梳理边缘智能发展脉络，构建多维分类框架。
Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons
- 按部署位置、算力能力等维度建立多维分类体系
- 涵盖从早期云边协同到当前设备端智能的演进路径
- 适合关注边缘计算与AI融合的研究者与工程师
边缘人工智能（Edge AI）将智能直接嵌入网络边缘设备，通过在数据源头附近处理数据，实现实时响应、提升隐私保护并降低延迟。本文遵循PRISMA指南，系统性地考察了边缘AI的演进历程、当前格局与未来方向，提出包含部署位置、处理能力（如TinyML、联邦学习）、应用领域及硬件类型在内的多维分类框架。分析追溯了该领域从早期内容分发网络与雾计算到现代设备端智能的发展轨迹。重点探讨了专用硬件加速器、优化软件及通信协议等核心技术。针对资源受限、安全风险、模型管理、功耗及连接性等挑战进行了批判性评估。同时指出类脑硬件、持续学习算法、云边协同及可信性集成等新兴机遇，为研究者与实践者提供全面参考框架。
原文摘要 · Abstract (English)
Edge Artificial Intelligence (Edge AI) embeds intelligence directly into devices at the network edge, enabling real-time processing with improved privacy and reduced latency by processing data close to its source. This review systematically examines the evolution, current landscape, and future directions of Edge AI through a multi-dimensional taxonomy including deployment location, processing capabilities such as TinyML and federated learning, application domains, and hardware types. Following PRISMA guidelines, the analysis traces the field from early content delivery networks and fog computing to modern on-device intelligence. Core enabling technologies such as specialized hardware accelerators, optimized software, and communication protocols are explored. Challenges including resource limitations, security, model management, power consumption, and connectivity are critically assessed. Emerging opportunities in neuromorphic hardware, continual learning algorithms, edge-cloud collaboration, and trustworthiness integration are highlighted, providing a comprehensive framework for researchers and practitioners.
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