摘要
随着边缘智能的兴起,协同推理技术通过云、边缘和终端设备之间的协作在提升智能应用的效率和性能方面取得了明显的进展。阐述了边缘智能的性能指标和应用场景及挑战,并以边缘智能的评级架构引出协同推理技术下的四种推理范式:端端协同、边端协同、边边协同和云边端协同推理。根据协同推理技术应用场景的局限性和差异性,对不同推理范式中协同推理技术的优势、局限性、原理及优化目标进行了全面分析对比。详细探讨了协同推理技术在不同应用场景下所解决的计算资源分配、推理时延优化和吞吐量优化等问题,指出了边缘智能中协同推理技术在隐私安全、通信服务资源管理、协同训练方面的挑战,并对其未来的发展趋势和研究方向进行了讨论,为该领域的研究提供参考和借鉴。
With the development of edge intelligence,collaborative inference technology has made significant progress in enhancing the efficiency and performance of intelligent applications through collaboration among cloud,edge,and terminal devices.The performance metrics,application scenarios,and challenges of edge intelligence are outlined,introducing four inference paradigms under collaborative inference technology through the rating architecture of edge intelligence:end-to-end collaboration,edge-to-end collaboration,edge-to-edge collaboration,and cloud-edge-end collaboration.Based on the limitations and differences of application scenarios for collaborative inference technology,the advantages,limitations,principles,and optimization goals of collaborative inference technology in different inference paradigms are comprehensively analyzed and compared.The discussion delves into issues such as computational resource allocation,inference latency optimization,and throughput optimization solved by collaborative inference technology in different application scenarios.It also points out challenges in privacy security,communication service resource management,and collaborative training within edge intelligence.Future development trends and research directions are discussed,providing references and insights for research in this field.
作者
赵婵婵
吕飞
石宝
尉晓敏
杨星辰
岳效灿
ZHAO Chanchan;LYU Fei;SHI Bao+;YU Xiaomin;YANG Xingchen;YUE Xiaocan(College of Information Engineering,Inner Mongolia University of Technology,Hohhot 010080,China)
出处
《计算机工程与应用》
北大核心
2025年第3期1-20,共20页
Computer Engineering and Applications
基金
内蒙古自治区自然科学基金(2023LHMS06016)
内蒙古自治区直属高校基本科研业务费项目(JY20240010,JY20230082)
内蒙古工业大学科学研究项目(BS201936)。
关键词
边缘计算
边缘智能
协同推理
机器学习
edge computing
edge intelligence
collaborative inference
machine learning
