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以太网 vs Infiniband的AI网络之争
傅里叶的猫· 2025-08-13 12:46
Core Viewpoint - The article discusses the competition between InfiniBand and Ethernet in AI networking, highlighting the advantages of Ethernet in terms of cost, scalability, and compatibility with existing infrastructure [6][8][22]. Group 1: AI Networking Overview - AI networks are primarily based on InfiniBand due to NVIDIA's dominance in the AI server market, but Ethernet is gaining traction due to its cost-effectiveness and established deployment in large-scale data centers [8][20]. - The establishment of the "Ultra Ethernet Consortium" (UEC) aims to enhance Ethernet's capabilities for high-performance computing and AI, directly competing with InfiniBand [8][9]. Group 2: Deployment Considerations - Teams face four key questions when deploying AI networks: whether to use existing TCP/IP networks or build dedicated high-performance networks, whether to choose InfiniBand or Ethernet-based RoCE, how to manage and maintain the network, and whether it can support multi-tenant isolation [9][10]. - The increasing size of AI models, often reaching hundreds of billions of parameters, necessitates distributed training, which relies heavily on network performance for communication efficiency [10][20]. Group 3: Technical Comparison - InfiniBand offers advantages in bandwidth and latency, with capabilities such as high-speed data transfer and low end-to-end communication delays, making it suitable for high-performance computing [20][21]. - Ethernet, particularly RoCE v2, provides flexibility and cost advantages, allowing for the integration of traditional Ethernet services while supporting high-performance RDMA [18][22]. Group 4: Future Trends - In AI inference scenarios, Ethernet is expected to demonstrate greater applicability and advantages due to its compatibility with existing infrastructure and cost-effectiveness, leading to more high-performance clusters being deployed on Ethernet [22][23].
AI 网络Scale Up专题会议解析
傅里叶的猫· 2025-08-07 14:53
Core Insights - The article discusses the rise of AI Networking, particularly focusing on the "Scale Up" segment, highlighting its technological trends, vendor dynamics, and future outlook [1] Group 1: Market Dynamics - The accelerator market is divided into "commercial market" led by NVIDIA and "custom market" represented by Google TPU and Amazon Tranium, with the custom accelerator market expected to gradually match the GPU market in size [3] - Scale Up networking is transitioning from a niche market to mainstream, with revenue projected to exceed $1 billion by Q2 2025 [3] - The total addressable market (TAM) for AI Network Scale Up is estimated at $60-70 billion, with potential upward revisions to $100 billion [12] Group 2: Technological Evolution - AI networking has evolved from "single network" to "dual network," currently existing in a phase of "multiple network topologies," with Ethernet expected to dominate in the long term [4] - The competition between Ethernet and NVLink is intensifying, with NVLink currently leading due to its maturity, but Ethernet is expected to gain market share over the decade [5] - Scale Up is defined as a "cache coherent GPU to GPU network," providing significantly higher bandwidth compared to Scale Out, with expectations of market size surpassing Scale Out by 2035 [8] Group 3: Performance and Cost Analysis - Scale Up technology shows a significant performance advantage, with latency for Scale Up products like Broadcom's Tomahawk Ultra at approximately 250ns, compared to 600-700ns for Scale Out [9] - Cost-wise, Scale Up Ethernet products are projected to be 2-2.5 times more expensive than Scale Out products, indicating a higher investment requirement for Scale Up solutions [9] Group 4: Vendor Strategies - Different vendors are adopting varied strategies in the Scale Up domain, with NVIDIA focusing on NVLink, AMD betting on UA Link, and major cloud providers like Google and Amazon transitioning towards Ethernet solutions [13] - The hardware landscape is shifting towards embedded designs in racks, with a potential increase in the importance of software for network management and congestion control as Scale Up matures [13]
ADI全面布局人形机器人
半导体芯闻· 2025-06-16 10:13
Core Viewpoint - The rise of humanoid robots has gained significant attention following a performance at a Spring Festival gala, highlighting advancements in embodied intelligence and the need for improved hardware, particularly chips, to overcome existing challenges [1] Group 1: Humanoid Robot Development - Humanoid robots are increasingly compared to upright vehicles, requiring perception systems, high-performance chips, and effective power management for extended operation [2] - The execution capabilities of humanoid robots differ from cars, as they must also manipulate objects with dexterity, particularly through their hands [2] Group 2: ADI's Role in Humanoid Robotics - ADI has been involved in the robotics market for years and is now accelerating its offerings, including traditional chips and subsystems to facilitate product design and implementation [4] - ADI provides a range of products for humanoid robots, including sensors, internal connection systems, motor control modules, and power management solutions [5] Group 3: Connection Technologies - GMSL (Gigabit Multimedia Serial Link) is highlighted as a key technology for internal connections in humanoid robots, offering efficient data transmission and improved performance [9] - ADI's GMSL solution supports real-time transmission of video, sensor data, and power, making it suitable for the complex requirements of humanoid robots [10] Group 4: Isolation and Control Solutions - ADI offers isolation devices to protect sensitive electronics in humanoid robots from electrical interference, ensuring reliable operation in challenging environments [10] - The ADMT4000 solution provides precise joint control for robotic arms, enabling memory of positions even after power loss, thus enhancing operational reliability [12][14] Group 5: Challenges in Dexterous Manipulation - The development of dexterous hands, referred to as "smart hands," is a critical challenge in the humanoid robotics industry, requiring advanced sensors and AI algorithms [15] - Simplifying internal connections within these dexterous hands is also a significant focus for developers [15]