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    面向边缘智能的大模型研究进展

    Research Progress on Large Models for Edge Intelligence

    • 摘要: 随着大模型技术的迅猛发展,大模型在自然语言处理和计算机视觉等领域表现出卓越的性能,成为解决复杂问题的重要工具,并在科研和产业界引发了广泛关注. 然而,当前基于云平台的大模型训练和推理方案面临诸多挑战,包括高昂的成本、有限的可扩展性和信息安全风险等. 随着模型参数规模的不断扩大,对于低成本、高效训练和推理的需求愈发迫切. 在端边侧进行大模型的协同训练和推理,可以显著降低延迟和带宽需求,同时增强数据隐私和操作效率,为大模型在多样化场景中的低成本应用提供关键技术支持,成为当前研究的热点之一. 全面调研了面向边缘智能的大模型相关研究,主要从大模型边缘训练和推理2个角度对当前相关研究进行了深入分析和讨论. 最后,提出了面向边缘智能的大模型技术发展所面临的挑战和未来展望. 希望能促进学术界和产业界对面向边缘智能的大模型技术有更深入了解和关注,并能够启发更多的学者开展深入研究.

       

      Abstract: With the rapid development of large-scale model technology, these models have exhibited remarkable performance in fields such as natural language processing and computer vision, becoming essential tools for addressing complex issues and drawing significant interest from both the scientific community and the industry. Nonetheless, current cloud-platform-based schemes for training and inference of large models face multiple challenges, including high expenses, restricted scalability, and information security risks. As the scale of model parameters expands continually, the need for low-cost, efficient training and inference methods grows ever more pressing. Carrying out collaborative training and inference of large models on edge devices can dramatically decrease latency and bandwidth demands, concurrently reinforcing data privacy and operational efficiency. This strategy furnishes vital technological support for the economical deployment of large models across a variety of contexts, thereby evolving into one of the prominent research hotspots. This article conducts a thorough investigation of research pertinent to large models in the context of edge intelligence, with an in-depth analysis and discourse primarily focused on two aspects: edge-based training and inference of large models. Ultimately, it outlines the challenges confronted in the progression of large model technologies tailored for edge intelligence and delineates future prospects. The ambition is to stimulate a heightened comprehension and intensified attention from both academic and industrial sectors towards technologies involving large models for edge intelligence, thereby encouraging further scholarly exploration in this thriving domain.

       

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