日前，一则重磅消息惊动全球半导体产业圈。 当地时间12月10日晚，全球半导体巨头高通公司宣布完成对 Ventana Micro Systems的收购，进一步强化 其在RISC-V领域的技术布局。 巨头加码RISC-V，印证了AI时代RISC-V开源架构的广阔前景。而在此背后，一座中国城市的战略布局 也浮出水面。 去年9月，Ventana携手珠海科技产业集团、跃昉科技在珠海牵头成立了全球首个基于RISC-V的DSA产业 创新合作组织——RDSA产业联盟。业内人士分析，随着Ventana正式纳入高通体系，该联盟有望接入高 通覆盖全球的技术、渠道与生态资源，迎来国际化发展的重要契机。 早在2023年12月召开的中共珠海市委九届六次全会上，珠海市委书记、省委横琴工委书记陈勇就提出 以"云上智城"、低空经济、开源体系等为重点大力培育新质生产力。2024年9月，陈勇书记进一步提 出，要借助RDSA产业联盟等平台，汇集全球产业链智慧和力量，将珠海打造为具有重要国际影响力和 示范性的RISC-V创新高地。 2025年RISC-V产业发展大会暨RDSA国际论坛在珠海、澳门两地举行 从前瞻布局"RISC-V+芯粒+DSA"高壁垒 ...

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].