在开源大模型持续丰富能力矩阵的行业背景下，AI产业链从底层基础设施到上层应用都正加速演进。 2026年被业界定义为AI应用大年，其中"物理AI"被多家头部厂商尤其看中，而底层基础设施在应用的差 异化需求催动下，也面临新的发展走向。 近日，Arm发布多项技术预测，指出2026年将迈入智能计算新纪元，届时，计算将具备更高的模块化特 性和能效表现，实现云端、物理终端及边缘人工智能 (AI) 环境的无缝互联。其中还提到，下一个价值 数万亿美元的AI平台将属于物理智能领域，智能能力将被植入新一代自主设备与机器人。 Arm中国区业务全球副总裁邹挺接受21世纪经济报道记者采访时指出，对于"物理AI"的发展，"业界完 全有能力打造出单台高性能的机器人或自动驾驶系统。但真正的挑战在于，如何实现数万甚至数百万台 同类设备的可靠部署。" 其背后直指原生AI硬件发展过程中面临的软硬件碎片化问题，由此，对于AI产业链来说，构建完善的 软件生态能力，同时部署充分灵活的异构计算硬件基础设施，成为布局关键所在。 物理AI疾驰 谈及今年备受关注的应用场景，"物理AI"必占其一。在前不久举行的CES 2026上，多家芯片头部厂商 高管都提到了今 ...

Core Viewpoint - The article discusses the transition of AI technology from centralized cloud computing to distributed edge deployment, emphasizing the importance of mobile devices in delivering intelligent user experiences. The launch of the Arm Lumex CSS platform is highlighted as a solution to performance bottlenecks in edge AI, enabling smarter, more efficient, and personalized experiences in consumer electronics [1][2][5]. Group 1: Industry Trends - AI technology is shifting from centralized cloud computing to distributed edge deployment, with mobile devices becoming the core carriers of intelligent experiences [1]. - The demand for low-latency, high-smoothness, and long-endurance edge AI is increasing, making edge AI a defining factor in product competitiveness [1]. - The edge computing industry faces challenges such as traditional architectures struggling to handle high-density AI tasks and increased chip design complexity leading to longer development cycles [1]. Group 2: Arm Lumex CSS Platform - Arm introduced the Lumex CSS platform, which integrates high-performance CPU, GPU, and system IP to address performance bottlenecks and development challenges in edge AI [2][5]. - The platform is designed for flagship smartphones and next-generation personal computers, aiming to optimize edge AI performance through technological innovation [7]. Group 3: Technical Innovations - The Arm C1 CPU cluster, a core component of the Lumex CSS platform, features the second-generation Scalable Matrix Extension (SME2) technology, enhancing AI workload performance by up to 5 times and energy efficiency by up to 3 times [8][10]. - The Mali G1-Ultra GPU, another key component, offers significant improvements in graphics and AI performance, including a 40% increase in game frame rates and a 20% boost in AI inference speed [18][22]. Group 4: Software Ecosystem - The KleidiAI software library is integrated with major AI frameworks, allowing developers to activate SME2 acceleration without code modifications, thus reducing development costs and barriers [26][29]. - The platform's design enables seamless integration of hardware capabilities with software, facilitating the large-scale deployment of edge AI [32][43]. Group 5: Market Impact - The global edge AI market is projected to grow from 321.9 billion yuan in 2025 to 1,223 billion yuan in 2029, with a compound annual growth rate of 39.6% [44]. - Arm's Lumex CSS platform represents a significant shift from traditional IP supplier to a full-stack solution provider, addressing industry pain points and enhancing the overall value chain [44][45].

Core Viewpoint - AI is recognized as the most significant technological innovation of the era, with Arm accelerating its integration across various platforms from cloud to edge [1] Industry Trends - Three major trends in AI development have been identified: 1. Miniaturization and performance enhancement of models, exemplified by models like DeepSeek achieving superior decision-making capabilities with a smaller footprint [3] 2. Explosive growth in edge computing, with the skepticism about edge AI largely dissipating as computational power continues to rise [3] 3. Acceleration of commercial applications for AI entities and physical AI, leading to innovative applications such as rescue robots and delivery robots [3] AI Readiness Index Report - Arm released the "AI Readiness Index Research Report," surveying 655 business leaders across eight global markets, with over 100 from China, highlighting the active AI application in smart manufacturing, technology, and energy sectors [3][4] Investment Characteristics in China - Chinese enterprises exhibit three characteristics in AI investment: 1. Strategy and budget prioritization, with 43% of Chinese companies having a clear AI strategy compared to 39% globally [4] 2. A focus on efficiency improvement as the core objective, with 95% planning to increase AI budget in the next three years [4] 3. Deployment of AI technologies primarily in chatbots, natural language processing, and deep learning, reflecting the robust development of large models in China [4] Challenges in AI Industry - The AI industry faces three core challenges: 1. Infrastructure issues, particularly the imbalance between energy consumption and computational power, with data center energy consumption rising from megawatt to gigawatt levels [5] 2. Data security concerns, with 48% of global enterprises worried about data privacy breaches, prompting Arm to implement advanced security measures [5] 3. Talent shortages, identified by 49% of respondents as a major barrier to AI development, leading Arm to create a broad developer ecosystem to alleviate this pressure [6] Collaboration in Large Language Models - Arm collaborates deeply with leading local large language model vendors, leveraging Armv9 architecture and KleidiAI to enhance AI performance, driving significant advancements in models like Tongyi Qianwen and Wenxin [7]