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].