发表机构
Pengcheng Laboratory; Harbin Institute of Technology (Shenzhen); Peking University（鹏城实验室; 哈尔滨工业大学（深圳）; 北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
综述大模型绿色发展，涵盖资源高效架构与软硬件协同设计，回顾高效模型构建、训练部署策略、节能硬件等进展，探讨其在关键领域应用，讨论挑战与方向，为大模型可持续发展提供路线图。
AI 中文摘要
大规模人工智能模型的迅速扩展在各个领域带来了显著的性能突破，但也引发了对计算成本、能源消耗和环境可持续性的关键担忧。本综述全面概述了大模型的绿色发展，强调资源高效架构和全栈软硬件协同设计。系统回顾了高效模型构建的进展，包括注意力算子优化、线性复杂度架构、模型稀疏化与合并，以及数据高效学习、参数高效微调、计算压缩等训练和部署策略。探讨了节能人工智能硬件，包括主流人工智能芯片、内存优化、跨平台部署和可持续基础设施。研究了大模型在深度搜索、遥感解释、国家规模基础设施和全球倡议等关键可持续性领域的应用。讨论了关键挑战和未来方向，强调持续学习范式、以内存为中心的硬件和标准化评估协议的必要性。本综述旨在为大模型的可持续、可扩展和对社会负责的发展提供全面路线图。
英文摘要
The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability. This survey provides a comprehensive overview of the green development of large models, emphasizing resource-efficient architectures and full-stack hardware-software co-design. We systematically review recent advances in efficient model construction, including attention operator optimization, linear-complexity architectures, and model sparsification and merging, as well as training and deployment strategies such as data-efficient learning, parameter-efficient fine-tuning, and computational compression. Beyond algorithmic improvements, we explore energy-efficient AI hardware, including mainstream AI chips, memory optimization, cross-platform deployment, and sustainable infrastructure. Furthermore, we examine how large models are being applied to sustainability-critical domains such as DeepSeek, remote sensing interpretation, national-scale infrastructure, and global initiatives. Finally, we discuss key challenges and future directions, highlighting the need for continual learning paradigms, memory-centric hardware, and standardized evaluation protocols. This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models. Paper homepage: https://cje.ejournal.org.cn/article/doi/10.23919/cje.2025.00.438
CommentsThis paper has been accepted by CJE (2026), paper homepage: https://cje.ejournal.org.cn/article/doi/10.23919/cje.2025.00.438
Journal refChinese Journal of Electronics, vol. 35, no. 5, pp. 1-24, 2026
