公众号记得加星标⭐️，第一时间看推送不会错过。 路透社首先报道了该公司在去年 12 月审判的密封证物中概述的计划，以及该公司在 2 月份从竞争对 手那里挖走高管的努力。 来源 ：内容 编译自路透社 。 知情人士周一表示， Arm Holdings 已聘请亚马逊人工智能芯片总监拉米·辛诺 (Rami Sinno) 来支持 其开发自主完整芯片的计划。 Sinno 负责帮助开发亚马逊自主研发的 AI 芯片 Trainium 和 Inferentia，旨在帮助构建和运行大型 AI 应用程序。 到目前为止，Arm 尚未自主研发芯片。相反，它设计并销售给客户的处理器的核心架构和指令集。 苹果和英伟达等芯片设计公司在其芯片中使用了 Arm 技术。 今年7月，Arm宣布计划将部分利润投入自有芯片及其他组件的制造。首席执行官Rene Haas讨论了 超越设计、构建芯片（更小、功能特定、可拼接的芯片版本）以及完整系统的可能性。 该公司由软银集团（SoftBank Group）持有多数股权，并向客户销售的芯片收取专利费。基于Arm 的设备几乎为全球所有智能手机提供支持，而基于Arm知识产权的服务器芯片已在长期由AMD和英 特尔主导 ...

半导体产业正处在前所未有之大变局时刻：一方面，摩尔定律渐趋极限；另一方面，人工智能的爆 发式增长对计算架构带来全新的机遇与挑战。面对这一趋势，Arm 于近期发布《芯片新思维：人 工智能时代的新根基》行业报告，该报告汇集了构建AI时代芯片设计新范式的洞察，涵盖算力、 能效、安全、可扩展性等核心议题，勾勒出未来智能计算的底层路线图。 在该报告中，Arm与业界专家分享了诸多关键趋势： AI计算底层逻辑发生质变 过去四十年，芯片技术经历了从早期的超大规模集成电路 (VLSI) 和极大规模集成电路 (ULSI) 设 计、移动芯片组的发展，到移动系统级芯片 (SoC) 的广泛应用，再到如今的 AI 优化的定制芯片 如果您希望可以时常见面，欢迎标星收藏哦~ 通过摩尔定律实现半导体缩放的传统方法已达到物理与经济的极限。产业正转向创新的替代 方案，如定制芯片、计算子系统 (CSS) 以及芯粒 (chiplets)，以持续提升性能与能效。 随着 AI 工作负载对计算密集型任务的需求日益增加，能效已成为 AI 计算发展的首要考量。 芯片设计正在整合优化的内存层次结构、先进的封装技术，以及成熟的电源管理技术，以降 低能源消耗，同时维 ...

Core Viewpoint - The semiconductor industry is undergoing unprecedented changes, driven by the limitations of Moore's Law and the explosive growth of artificial intelligence (AI), which presents new opportunities and challenges for computing architecture [1][2]. Group 1: Evolution of Chip Technology - Over the past four decades, chip technology has evolved from early VLSI and ULSI designs to mobile chipsets, and now to AI-optimized custom chip solutions, significantly impacting chip architecture and industry strategies [2]. - The traditional methods of scaling semiconductors through Moore's Law have reached physical and economic limits, prompting a shift towards innovative alternatives like custom chips, computing subsystems (CSS), and chiplets to enhance performance and energy efficiency [3][6]. Group 2: AI and Energy Efficiency - The demand for energy efficiency has become paramount in AI computing, as AI workloads increasingly require intensive computational tasks [3][9]. - The report emphasizes a "full-stack optimization path" to address the dual challenges of computing power and energy efficiency, involving collaboration with foundries and optimizing various layers from transistors to data center operations [18]. Group 3: Custom Chips and Market Dynamics - Custom chips are emerging as a crucial solution to meet diverse application needs, with major cloud service providers accounting for nearly half of global cloud server procurement spending in 2024 [8][10]. - The rise of chiplets is facilitating the widespread adoption of custom chips, allowing manufacturers to enhance performance without redesigning entire chips, thus accelerating time-to-market [11][12]. Group 4: Security and Collaboration - As AI technology evolves, so do security threats, necessitating a multi-layered hardware and software defense system to counter AI-driven cyberattacks [3][20]. - Successful chip design increasingly relies on close collaboration among IP providers, foundries, and system integrators, alongside system-level optimizations and standardized interfaces to support modular designs [20][22]. Group 5: Future Outlook - The future of chip design will depend on the integration of various processing units (CPU, GPU, TPU) to support different workloads, with a focus on creating a sustainable ecosystem that leverages the strengths of all industry players [20][22]. - Arm's commitment to standardization and collaboration is expected to drive the next generation of AI computing architectures, ensuring rapid innovation and widespread adoption [22][23].