Core Insights - The article highlights the launch of the Lingyu CPU processor by Ruisi Semiconductor, marking it as China's first fully self-developed high-performance RISC-V architecture server chip, aimed at high-performance computing and applications like DeepSeek [2][4]. Company Overview - Ruisi Semiconductor, established in 2018, focuses on developing processors based on the open-source RISC-V architecture, providing chip solutions for various sectors from edge computing to data centers [4]. - The company has secured 37 invention patents, 19 software copyrights, and 6 integrated circuit layout designs related to the Lingyu processor [4]. Product Details - The Lingyu processor features a "one chip, dual-core" design architecture, consisting of 32 high-performance general computing cores (CPU) and 8 dedicated intelligent computing cores (LPU), optimized for tasks like inference for open-source large language models [2]. - The performance of the Lingyu processor is claimed to be on par with mainstream server chips from Intel and AMD [2]. Market Potential - The global intelligent computing market is projected to reach $39 billion in 2024 and grow to $89 billion by 2029, with a CAGR of 17.8%. The Chinese intelligent computing market is expected to exceed 150 billion RMB by 2028, with an annual growth rate of 21% [2]. - The global high-performance computing (HPC) market is anticipated to surpass $40 billion and potentially double by 2032 [2]. Ecosystem and Partnerships - The Lingyu processor ecosystem includes over 50 partners, covering key component manufacturers, OEMs, system software, and industry solutions, with successful software adaptations for various operating systems [4]. - Notable partners include Lenovo, SenseTime, Jiangbolong, and XSKY, supporting mainstream databases, machine learning frameworks, and complex deployment scenarios [4]. Future Plans - The company aims to expand its product matrix and accelerate the large-scale deployment of RISC-V server chips, contributing to a secure and efficient domestic computing ecosystem [5].

图片来源： Ampere 软银集团 3 月 19 日宣布，将通过一笔全现金交易，以 65 亿美元收购由前英特尔高管 Renee James 创立的芯片设计公司 Ampere Computing ， 此举旨在扩大其在人工智能基础设施领域的投资。交易 完成后， Ampere 将作为软银的全资子公司运营，预计交易将在 2025 年下半年完成。 凯雷集团和甲骨文公司，作为 Ampere 的主要投资者，将出售他们在加州圣克拉拉这家初创公司的股 份。 根据软银的声明，凯雷持有 59.65% 的股份，而甲骨文持有 32.27% 。 这家初创公司雇佣了 1000 名 半导体工程师。 据彭博社报道， 2021 年，软银曾考虑收购 Ampere 的少数股权，当时其估值达 80 亿美元。 软银是 Arm Holdings 的最大股东，而 Ampere 基于 ARM 计算平台开发了服务器芯片，这使两家公司 成为强有力的合作伙伴。 图片来源：软银 软银于 2016 年以 320 亿美元收购了英国芯片设计公司 Arm ，该公司于 2023 年上市。 Ampere 的客 户包括 Google Cloud 、 Microsoft Azure ...

Core Viewpoint - The article discusses the advancements in humanoid robotics and the role of NVIDIA in developing the necessary technologies, particularly focusing on the concept of "Physical AI" and the importance of simulation data for training robots [1][7][41]. Group 1: NVIDIA's Role in Robotics - NVIDIA is positioning itself as a key player in the humanoid robotics industry by developing a series of platforms and models, including the Cosmos training platform and the Isaac GR00T N1 humanoid robot model [3][4][19]. - The company has created a comprehensive ecosystem for humanoid robot development, including high-performance computing (DGX), simulation platforms (Omniverse), and inference chips (Jetson Thor) [19][31]. - NVIDIA's strategy involves not only selling hardware but also providing software tools and services to enhance the capabilities of humanoid robots [41][42]. Group 2: The Concept of Physical AI - The term "Physical AI" refers to the next wave of AI development, where robots are expected to understand physical laws and interact with the real world autonomously [8][41]. - Unlike traditional industrial robots that perform specific tasks, humanoid robots aim to understand and make decisions based on their environment, showcasing a significant leap in intelligence [10][13]. - The training of these robots requires vast amounts of simulation data that mimic real-world physics, filling the gap where real-world data is scarce [16][17][18]. Group 3: Simulation Data and Its Importance - Simulation data is crucial for training humanoid robots, as it allows for the creation of realistic scenarios that adhere to physical laws, which is essential for effective learning [16][18]. - The article compares real data to "real exam questions" and simulation data to "mock exams," emphasizing the need for high-quality simulation data to ensure effective training [18]. - NVIDIA's experience in gaming and simulation technologies positions it well to provide the necessary tools for creating this simulation data [23][30]. Group 4: Historical Context and Future Directions - NVIDIA's journey in high-performance computing has evolved from gaming to various high-value applications, including mobile devices, autonomous driving, and now humanoid robotics [32][39]. - The company has learned from past ventures, such as its experience with mobile processors, to focus on more promising markets like AI and robotics [36][38]. - As the demand for "Physical AI" grows, NVIDIA aims to solidify its position by offering integrated solutions that combine hardware and software for the robotics industry [41][43].

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The white paper discusses the urgent need for high-performance networks to support the growing demands of AI and HPC, particularly in terms of scalability, stability, and performance [5][6] - It highlights the challenges faced by high-performance data center networks (HP-DCN) and high-performance wide area networks (HP-WAN) in meeting the requirements of large-scale AI model training and high-performance computing [11][23] - The report emphasizes the importance of a multi-dimensional automated operation and maintenance system to ensure network reliability and performance [17][19] - It outlines the necessity for scalable security mechanisms to protect sensitive data in increasingly large and complex network environments [21][22] Summary by Sections 1. Introduction - The introduction outlines the transformative impact of AI and HPC on network technology, emphasizing the need for enhanced network performance to support large-scale AI model training [5][6] 2. Terminology and Abbreviations - A list of relevant abbreviations and their meanings is provided, which aids in understanding the technical content of the report [7][9] 3. Key Requirements and Challenges of High-Performance Networks - **High-Performance Data Center Networks (HP-DCN)**: The report discusses the need for ultra-large-scale networking capabilities, highlighting challenges such as switch access capacity limitations and network topology constraints [11][12] - **Ultra-High Stability**: Stability is crucial for distributed systems like AI and HPC, with metrics for network availability and performance consistency being essential [13][14] - **Extreme Performance**: The report details the need for ultra-low latency, minimal jitter, and effective high throughput to maximize cluster computing efficiency [15][16] - **Multi-Dimensional Automated Operation and Maintenance**: A comprehensive monitoring system is necessary to address the complexities of AI model training networks [17][18] - **Scalable Security Mechanisms**: The report stresses the importance of integrating security into network operations to protect sensitive data [21][22] 4. High-Performance Network Technology Architecture - **Current Status and Trends**: The architecture of intelligent computing center networks is evolving, with a focus on end-to-end integration to enhance performance [26][27] - **ZTE's High-Performance Network Architecture**: ZTE's architecture aims to support ultra-large-scale, high-throughput, and low-latency networks while ensuring operational efficiency [30][31] 5. Key Technologies for High-Performance Data Center Networks - **Ultra-Large-Scale Networking Technologies**: The report discusses the need for high-capacity switches and scalable routing protocols to support large GPU clusters [36][39] - **Routing Protocols**: Various routing protocols are evaluated for their effectiveness in large-scale intelligent computing centers, with BGP and RIFT being highlighted [48][50]

香港交易及結算所有限公司及香港聯合交易所有限公司對本公告的內容概不負 責，對其準確性或完整性亦不發表任何聲明，並明確表示，概不對因本公告全 部或任何部份內容而產生或因倚賴該等內容而引致的任何損失承擔任何責任。 ASMPT LIMITED （於開曼群島註冊成立之有限公司） （股份代號：0522） 二零二四年第三季度業績新聞稿 有關 ASMPT Limited 及其附屬公司截至二零二四年九月三十日止九個月業績的 新聞稿附載於本公告。 承董事會命 董事 黃梓达 香港，二零二四年十月三十日 於本公告日期，本公司董事會成員包括獨立非執行董事： Orasa Livasiri 小姐（主 席）、樂錦壯先生、黃漢儀先生、鄧冠雄先生、張仰學先生及蕭潔雲女士；非 執行董事： Hichem M'Saad 博士及 Paulus Antonius Henricus Verhagen 先生；執行 董事：黃梓达先生及 Guenter Walter Lauber 先生。 ASMPT 的董事對本公告所載資料的準確性共同及個別地承擔全部責任，並在作 出一切合理查詢後確認，據其所知，本公告所表達之意見乃經審慎周詳考慮後 始行發表。本公告並無遺漏其他 ...