如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容 来自 technews ，谢谢。 陈立武在3月27日发布的英特尔年度报告中提到，英特尔2024年的表现确实未达到股东们的期望， 他将持续推动转型，并承诺打造更强大的产品业务和晶圆代工业务。 陈立武也在年度报告中表示，将强化英特尔在云端AI数据中心市场的地位；有鉴于客户对更低成 本 、 更 高 效 能 运 算 的 显 著 需 求 ， 英 特 尔 必 须 开 发 具 竞 争 力 的 机 架 级 系 统 解 决 方 案 （ rack-scale system solutions），「这将会是我和团队的关键优先事项。」 参考链接 https://technews.tw/2025/04/22/lip-bu-tan-may-computex-taiwan/ 点这里加关注，锁定更多原创内容 美国芯片大厂英特尔（Intel）正逢多事之秋，新CEO陈立武3月上任后大刀阔斧改革，根据供应链 消息，英特尔将于5 月中旬于台北国际电脑展（COMPUTEX）开展前举办在台40周年晚宴，陈立 武也将亲自来台与供应链伙伴会面，固桩意味浓厚。 据了解，陈立武不会在5月20日至23日举行的 ...

人工智能（AI）技术的快速发展掀起了新一轮工业革命浪潮，通用大模型的出现让AI技术从专用化迈 向了通用化。而工业场景中，边缘AI（Edge AI）备受关注，并发挥着越来越重要的作用。 顾名思义，边缘AI计算就是指在边缘计算环境中实现人工智能的一种方法。相比于传统的云端AI，边 缘AI具有将计算和推断能力推向离数据源更接近的位置的优势，可以提供了更快速、更安全、更隐私 保护的数据处理和决策能力，使得人工智能能够更好地应用于各种边缘设备和应用场景中，亦更适合边 缘工业场景应用。 据Gartner预计，2025年将有75%的数据产生于数据中心和云之外，边缘计算不需要将数据传输回数据中 心，在应对超低延时和海量数据的挑战时具有明显优势，因此其重要性日益凸显。 作为仪器仪表领域的领军企业，华盛昌紧跟趋势，推出应用边缘AI计算技术的测量仪器仪表智能体 ——AI智能电弧故障检测解决方案，并于4月15日与德州仪器（TI）共同举办了该新品的联合发布会。 据了解，该产品通过应用边缘AI计算技术，在精准风险分层控制、全场景覆盖能力、系统可靠性及法 规兼容性等多方面均实现跃升，解决了传统逆变器无法应对的屋顶电站安全隐患，实现光伏与储 ...

【MoneyDJ】 驱动IC厂商联咏公告Q1营收为271.2亿新台币（约人民币60.31亿元），季增 7.3%、年增11%，符合先前法说会提及单季营收可望季增约7%的预期。 ▶ 关于集邦 而尽管接下来全球经济可能受到美国加征关税的影响，联咏仍将在Q2如期推出OLED TDDI（显 示触控整合驱动芯片）的新产品，未来也将持续卡位边缘AI所带来的各项装置商机。 图片来源：联咏 联咏看好边缘AI装置渗透率提升趋势，未来也将扩大投入机器视觉、智能居家等应用领域。 公司 并强调，其部分产品已具备先进制程量产能力，将随AI装置逐步落地进一步推升出货动能。 而针对关税后续对于联咏营运的影响，法人则指出，联咏作为IC设计公司，将持续聚焦灵活调整 产销配置，以确保出货稳定与供应链韧性。 上下滑动查看 ...

Market Overview - The main board saw significant gains with notable stocks such as Guanghe Technology (+10.01%), Huati Technology (+10.00%), and Xuguang Electronics (+10.00%) leading the charge [1] - The ChiNext board also performed well, with stocks like Fulede (+9.74%) and Jiebang Technology (+5.91%) showing strong increases [1] - The Sci-Tech Innovation board was led by Guoguang Electric (+18.90%) and Kesi Technology (+9.61%) [1] - Active sub-industries included SW Semiconductor Equipment (+2.44%) and SW Military Electronics III (+1.86%) [1] Domestic News - BYD reported strong growth in the new energy vehicle sector, with March 2025 production reaching 395,091 units, a year-on-year increase of 33.36%, and sales of 377,420 units, up 24.78% [1] - The China Economic Media Association held a meeting to discuss challenges facing private enterprises in the current economic climate [1] - The China Academy of Information and Communications Technology reported that the domestic smartphone market continued to grow, with total shipments reaching 19.662 million units in February 2025, a year-on-year increase of 37.9% [1] - The global semiconductor industry is expected to increase investment in wafer fabrication equipment, with spending projected to reach $110 billion in 2025, marking six consecutive years of growth [1] Company Announcements - Luxshare Precision reported a decrease in convertible bonds due to conversion, with a remaining balance of approximately 2.999 billion yuan as of March 31, 2025 [3] - Sanlipu's 2024 annual report showed total revenue of 2.590 billion yuan, a year-on-year increase of 25.25%, and a net profit of 68 million yuan, up 59.07% [3] - Tonglian Precision announced a share buyback of 909,527 shares, representing 0.5676% of total equity, with a total expenditure of approximately 18.894 million yuan [3] - Aohai Technology reported a total buyback of 1,793,300 shares, accounting for 0.65% of total equity as of March 31, 2025 [3] International News - Japan's Ministry of Economy, Trade and Industry announced additional funding of 80.25 billion yen to semiconductor manufacturer Rapidus, a key initiative for revitalizing the domestic semiconductor industry [4] - Intel's CEO announced plans to launch the Panther Lake processor based on 18A technology, focusing on building a product ecosystem for independent software developers and AI applications [4] - Rapidus plans to start trial production of 2nm chips in April 2025, with a goal for mass production by 2027 [4] - Reports regarding Google closing the Android Open Source Project (AOSP) were clarified, indicating adjustments in open-source strategies rather than a complete shutdown [4]

Core Insights - The Edge AI Foundation has rebranded from the TinyML Foundation and released the "2025 Edge AI Technology Report," highlighting the maturity and real-world applications of TinyML [1][3]. Group 1: Edge AI Technology Drivers - The report discusses advancements in hardware and software that support Edge AI deployment, focusing on innovations in dedicated processors and ultra-low power devices [3]. - Edge AI is transforming operational models across various industries by enabling real-time analysis and decision-making capabilities [3]. Group 2: Industry Applications of Edge AI - In the automotive sector, Edge AI enhances safety and response times, with examples like Waymo and NIO utilizing real-time data processing for improved performance [7][8]. - Manufacturing benefits from Edge AI through predictive maintenance, quality control, and process optimization, with reported reductions in maintenance costs by 30% and downtime by 45% [9][12]. - In healthcare, localized AI accelerates diagnostics and improves patient outcomes by analyzing medical data directly on devices [14]. - Retail operations are optimized through real-time behavior analysis and AI-driven systems, reducing checkout times by 30% [16]. - Logistics is enhanced by integrating Edge AI with IoT sensors, allowing for immediate analysis of data and optimization of supply chain operations [18]. - Smart agriculture utilizes Edge AI for precision farming, reducing water usage by 25% and pesticide use by 30% [21]. Group 3: Edge AI Ecosystem and Collaboration - The Edge AI ecosystem relies on collaboration among hardware vendors, software developers, cloud providers, and industry stakeholders to avoid fragmentation [24]. - A three-layer architecture is recognized for Edge AI, distributing workloads across edge devices, edge servers, and cloud platforms [24][25]. - Cross-industry partnerships are increasing, with companies like Intel and Qualcomm collaborating to enhance Edge AI deployment [26][27]. Group 4: Emerging Trends in Edge AI - Five emerging trends are reshaping Edge AI, including federated learning, quantum neural networks, and neuromorphic computing [30]. - Federated learning is expected to enhance model adaptability and collaboration across industries, with a projected market value of nearly $300 million by 2030 [31]. - Quantum computing is set to redefine Edge AI capabilities, enabling faster decision-making and real-time processing [34][36]. - AI-driven AR/VR applications are evolving with Edge AI, allowing for real-time responses and improved energy efficiency [39]. - Neuromorphic computing is gaining traction for its energy efficiency and ability to handle complex tasks without cloud connectivity [41].

Core Viewpoint - Arm predicts that AI inference will soon be ubiquitous, enhancing its embedded platform with the first 64-bit Armv9 CPU core designed for edge workloads [1][2]. Group 1: Product Introduction - Arm has launched the Cortex-A320 CPU core, which is the first ultra-efficient Cortex-A processor based on the Armv9 architecture, designed for edge AI applications [7][14]. - The Cortex-A320 is described as the "smallest Armv9 implementation," featuring an AArch64 instruction set and a relatively simple single-issue, out-of-order, eight-stage core [2][3]. Group 2: Performance Enhancements - The new Cortex-A320 offers over eight times the machine learning performance compared to last year's platform and can handle large AI models with over one billion parameters [3][13]. - Compared to the Cortex-A520, the Cortex-A320 achieves more than 50% efficiency improvement through various microarchitecture updates [7][8]. Group 3: Memory and Efficiency - The Cortex-A320 is designed to address the increasing memory size requirements driven by the demand for efficient execution of larger networks, supporting more addressable memory than Cortex-M based platforms [4][10]. - It is reported to be the most energy-efficient processor in the Armv9 series, using only half the power of the Cortex-A520 in some reference designs [4][11]. Group 4: Software and Ecosystem Support - Arm provides support for new edge hardware through its Arm Kleidi library, which includes computing kernels for AI frameworks and computer vision applications [4][6]. - The Cortex-A320 supports real-time operating systems like FreeRTOS and Zephyr, as well as Linux, enhancing its flexibility for various applications [5][12]. Group 5: Security Features - The Cortex-A320 incorporates advanced security features from the Armv9 architecture, including memory tagging extensions for enhanced memory safety and pointer authentication to mitigate programming attacks [11][14]. - It also supports secure EL2 for improved software isolation in edge devices, contributing to the overall security of IoT and embedded systems [11][14]. Group 6: Market Applications - The Cortex-A320 is suitable for a wide range of applications, including IoT devices, smart wearables, and server infrastructure management controllers [5][11]. - Its design allows for scalability from single-core to quad-core configurations, making it adaptable for various performance needs [9][10].

Financial Data and Key Metrics Changes - Group revenue for the first half of 2024 reached RMB 1,740 million, representing a 21.4% increase year-on-year [6] - Generative AI revenue surged to RMB 1,050 million, accounting for 60% of total group revenue, up from 21% last year [12] - EBITDA loss reduced by 26.5% and overall loss decreased by 21.2% in the first half of 2024 [8][42] - Gross profit margin remained at 44%, consistent with the previous year [42] Business Line Data and Key Metrics Changes - Generative AI revenue increased by 256% year-on-year, becoming the primary driver of revenue growth [39] - Sensors revenue doubled to RMB 1,168 million, accounting for 10% of group revenue [12] - Traditional AI revenue was RMB 520 million, contributing 30% of group revenue, indicating a decline [40] Market Data and Key Metrics Changes - Overseas market revenue grew by 40% year-on-year, now accounting for 18% of total revenue [13][41] - The Chinese intelligent computing services market is projected to grow at a CAGR of over 50% for the next five years, reaching nearly RMB 200 billion by 2028 [21] Company Strategy and Development Direction - The company is focused on generative AI, leveraging deep synergies between large models and infrastructure to enhance model capabilities and reduce costs [7][39] - The strategic pivot towards generative AI has been more successful than anticipated, with significant growth in various sectors including intelligent hardware, electric vehicles, and finance [10][12] - The company aims to expand its operational computing power to 25,000 petabytes by the end of the year [19] Management Comments on Operating Environment and Future Outlook - Management expressed optimism about the generative AI market, highlighting its rapid growth and the need for companies to invest in large models [9][76] - The competitive landscape is described as fierce, with significant investments required to maintain competitiveness [8][39] - Management emphasized the importance of balancing long-term growth with short-term investments [8] Other Important Information - The company has deployed over 50,000 GPUs, with total computing power exceeding 20,000 petabytes, positioning it as a key player in the AI infrastructure market [18] - The SESNOVA large model series has shown significant improvements, with version 5.5 released in July 2024, enhancing capabilities and real-time interaction [24][25] Q&A Session Summary Question: What are the potential applications for edge AI in collaboration with smartphone manufacturers? - Management is optimistic about edge AI prospects, emphasizing the growth of the user base and the potential for new application models beyond smartphones, including IoT devices [52][54] Question: How is the company planning to scale computing power resources? - The company is focusing on improving operational efficiency while expanding computing power, adopting a strategic approach to maintain competitiveness [58][59] Question: What are the core capabilities of the next generation large model? - Management discussed the importance of reasoning and high-order data in enhancing model capabilities, emphasizing the need for better data and model architecture [63][66] Question: Which products or services predominantly contribute to the increase in generative AI revenue? - The company is focusing on the commercialization of its technological expertise in AI infrastructure and large models, which has led to significant growth in generative AI revenue [72][76] Question: What is the current progress in commercializing end-to-end algorithms in the autonomous driving sector? - The company is dedicated to a pure visual technology path for autonomous driving, leveraging its computational resources to support automakers in developing advanced driving technologies [81][84]