28款车型齐聚，两款全球首秀 "这届上海车展上感觉有一半人都是国际友人。" 4月23日，时值2025（第21届）上海国际汽车工业展览会首日，一些参展的媒体人如此感慨。 展会上国际面孔的增多，一方面说明着中国的汽车开始更多被全球关注，另一方面，也是国际品牌们更 加重视中国市场的具体表现。其中，梅赛德斯-奔驰就以本次车展为契机，大秀新平台、新产品、新技 术，俨然将上海车展办成了奔驰的春晚。 从去年以来，针对竞争愈加激烈的中国市场，国际品牌们纷纷交出自己的应对答案，而这一次的上海车 展上的众多大招，便是奔驰应对中国市场变局、继续深度连接中国的答案。 本次上海车展，奔驰携旗下梅赛德斯-奔驰、梅赛德斯-AMG、梅赛德斯-迈巴赫和G级越野车全品牌共28 款车型亮相。 其中，MMA平台的首款国产车型——全新梅赛德斯-奔驰纯电长轴距CLA，以及面向未来的顶级豪华 MPV车型Vision V概念车在本次车展上完成全球首秀，全新梅赛德斯-AMG GT 63 4MATIC+正式上市。 全新纯电长轴距CLA车型是奔驰针对中国市场智能与豪华变革之下的答案之作。 在奔驰的体系中，这款车是迄今为止最智能的奔驰。新车搭载了奔驰自研全新架构M ...

Core Viewpoint - Researchers from the National University of Singapore (NUS) have demonstrated that a single standard silicon transistor can mimic the behavior of biological neurons and synapses, bringing hardware-based artificial neural networks (ANN) closer to reality [1][2]. Group 1: Research Findings - The NUS research team, led by Professor Mario Lanza, has provided a scalable and energy-efficient solution for hardware-based ANN, making neuromorphic computing more feasible [1][2]. - The study published in Nature on March 26, 2025, highlights that the human brain, with approximately 90 billion neurons and around 100 trillion connections, is more energy-efficient than electronic processors [1][2]. Group 2: Neuromorphic Computing - Neuromorphic computing aims to replicate the brain's computational capabilities and energy efficiency, requiring a redesign of system architecture to perform memory and computation in the same location [2]. - Current neuromorphic systems face challenges due to the need for complex multi-transistor circuits or emerging materials that have not been validated for large-scale manufacturing [2]. Group 3: Technological Advancements - The NUS team has shown that a single standard silicon transistor can replicate neural firing and synaptic weight changes by adjusting the resistance of the terminal to specific values [3]. - They developed a dual-transistor unit called "Neuro-Synaptic Random Access Memory" (NS-RAM), which operates in neuron or synapse states [3]. - The method utilizes commercial CMOS technology, ensuring scalability, reliability, and compatibility with existing semiconductor manufacturing processes [3]. Group 4: Performance and Applications - The NS-RAM unit demonstrated low power consumption, stable performance over multiple operational cycles, and consistent, predictable behavior across different devices, essential for building reliable ANN hardware for practical applications [3]. - This breakthrough marks a significant advancement in the development of compact, energy-efficient AI processors, enabling faster and more responsive computing [3].

Core Viewpoint - Researchers from the National University of Singapore (NUS) have demonstrated that a single standard silicon transistor can mimic the behavior of biological neurons and synapses, bringing hardware-based artificial neural networks (ANN) closer to reality [1][3]. Group 1: Research Findings - The NUS research team, led by Professor Mario Lanza, provides a scalable and energy-efficient solution for hardware-based ANN, making neuromorphic computing more feasible [1][3]. - The study published in Nature on March 26, 2025, shows that a single silicon transistor can replicate neural firing and synaptic weight changes, which are fundamental mechanisms of biological neurons and synapses [3][4]. Group 2: Technical Innovations - The research achieved this by adjusting the resistance of the transistor to specific values, controlling two physical phenomena: impact ionization and charge trapping [4]. - The team developed a dual-transistor unit called "neuro-synaptic random access memory" (NS-RAM), which operates in neuron or synapse states [4]. Group 3: Advantages of the New Approach - The method utilizes commercial CMOS technology, ensuring scalability, reliability, and compatibility with existing semiconductor manufacturing processes [4]. - Experimental results show that NS-RAM units exhibit low power consumption, stable performance over multiple operational cycles, and consistent behavior across different devices, essential for building reliable ANN hardware [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 - The article highlights the significance of the 2025 Jiufengshan Forum as a premier event in the global compound semiconductor industry, focusing on innovation and technological advancements across various sectors [2][4]. Group 1: Forum Overview - The 2025 Jiufengshan Forum will take place from April 23-25 at the Wuhan Optics Valley Convention and Exhibition Center, featuring keynote speeches and 11 parallel forums covering topics from key materials to AI-enabled EDA toolchains [2][4]. - Over 100 high-quality reports have been confirmed for the forum, with early bird ticket sales ending on March 20 [4]. Group 2: Key Themes of Parallel Forums - The forum will address cutting-edge technologies such as neuromorphic computing, two-dimensional material devices, silicon photonic quantum integration, and wide bandgap semiconductors, focusing on disruptive technologies and industry development directions [5]. - A comprehensive ecosystem will be constructed, covering the entire chain from material preparation to system integration, promoting collaborative innovation within the semiconductor industry [6]. - The integration of heterogeneous technologies will unlock multiplier effects in technological innovation, combining materials, packaging, and circuit design [7]. - The forum will showcase breakthroughs in domestic technologies, including transmission electron microscopes and compound semiconductor equipment, revealing paths to overcome technical barriers [8]. - Insights into emerging demands in sectors like 5G communication, smart driving, and quantum computing will be shared, along with semiconductor technology solutions for scenarios such as electric vehicles and data centers [9]. - The event will gather leading global companies, research institutions, and investment firms to discuss key topics such as third-generation semiconductor capacity layout and testing technology standards [10].

Core Viewpoint - The rapid development of artificial intelligence (AI) is pushing the limits of traditional computing technologies, necessitating sustainable and energy-efficient solutions for exponential scaling of parallel computing systems [1][2][30]. Group 1: Technological Advancements - The article emphasizes the importance of optimizing the entire system from software and system architecture to silicon and packaging to maximize performance, power consumption, and cost [2]. - Key technologies such as RibbonFET and PowerVia are highlighted for their potential to enhance performance and efficiency in semiconductor design [4][5]. - High NA EUV technology is noted for its ability to simplify electronic design automation (EDA) and improve yield and reliability [7][8]. Group 2: 3D Integration and Packaging - 3D Integrated Circuits (3DIC) are crucial for achieving higher computational power in smaller areas while reducing energy consumption [11]. - The need for advanced packaging techniques to enhance interconnect density and energy efficiency is discussed, with a focus on modular design environments [12][15]. - The integration of glass in packaging to scale interconnect geometries and improve power transmission efficiency is identified as a significant technological advancement [14]. Group 3: Power Delivery and Efficiency - The article discusses the increasing power demands for AI workloads and the limitations of traditional motherboard voltage regulators (MBVR) [21][22]. - Fully Integrated Voltage Regulators (FIVR) are proposed as a solution to improve power conversion efficiency by bringing voltage regulation closer to the chip [23][24]. - The potential of pairing high-voltage switch-capacitor voltage regulators with low-voltage integrated voltage regulators for enhanced power density and efficiency is explored [24]. Group 4: Software and Ecosystem Collaboration - Software is deemed a critical component of the innovation matrix, requiring collaboration within the open-source ecosystem to enhance security and streamline processes [25]. - The need for industry-wide collaboration to develop next-generation advanced computing systems is emphasized, ensuring alignment with market demands and sustainability [28]. Group 5: Industry Challenges and Opportunities - The article outlines the challenges faced in achieving exponential performance improvements for AI, including power, connectivity, and cost issues [30]. - It calls for innovative approaches across various domains, including process technology, 3DIC system design, and power delivery, to meet the industry's computational demands [30].

Core Viewpoint - The ongoing US-China chip trade war has led to a significant increase in China's research output in next-generation chip manufacturing, surpassing that of the US by more than double, indicating a potential shift in technological capabilities in the near future [2][4][5]. Research Output Comparison - From 2018 to 2023, China produced 34% of the global research papers on chip design and manufacturing, while the US and Europe contributed only 15% and 18%, respectively [10]. - In terms of high-citation papers, 50% of the top 10% most cited articles in this field were authored by Chinese researchers, compared to 22% from the US and 17% from Europe [5][10]. Emerging Technologies - China's research focus includes neuromorphic computing and optoelectronic computing, which are considered post-Moore's Law technologies and are less affected by current export restrictions [2][6]. - The research indicates that if these emerging technologies are commercialized, they could significantly enhance China's position in the global chip market, making it difficult for the US to maintain its competitive edge through export controls [7][8]. Impact of US Export Controls - Since October 2022, the US has imposed restrictions on the sale of advanced chips and manufacturing equipment to China, aiming to limit China's ability to produce cutting-edge chips [6][3]. - Despite these restrictions, China's research output continues to grow, with significant contributions from leading institutions such as the Chinese Academy of Sciences and Tsinghua University [11][13]. Research Trends and Hotspots - The overall growth in chip research from 2018 to 2023 was 8%, which is slower compared to more popular fields like artificial intelligence [9]. - The research hotspots identified include neuromorphic computing and photonics, reflecting a shift towards innovative semiconductor technologies that address the limitations of traditional chip manufacturing [15][16].

Core Viewpoint - China is leading in foundational research for next-generation computing, raising concerns that U.S. export controls may become ineffective if these research results are commercialized [1][4]. Group 1: Research Output - From 2018 to 2023, China published 160,852 semiconductor-related papers, more than double the U.S. output of 71,688 papers [1]. - China's semiconductor research papers grew by 41% during this period, significantly outpacing India (26%), the U.S. (17%), and South Korea (6%) [1]. - In terms of impactful research, China authored 23,520 papers in the top 10% of citations, nearly half of the total, compared to the U.S. with 10,300 papers [2]. Group 2: Institutional Strength - Among the top 10 institutions publishing semiconductor research from 2018 to 2023, 9 are Chinese [2]. - The report from the Korea Institute of Science and Technology Evaluation and Planning indicates that South Korea's semiconductor technology capabilities are rated lower than China's across various fields [3]. Group 3: Future Implications - U.S. export controls on advanced semiconductors and manufacturing equipment may struggle to contain China's growth as it shifts focus to new semiconductor architectures [4]. - New fields such as neuromorphic computing and optical computing are identified as key growth areas for China's semiconductor research [4]. - Analysts predict that if China successfully commercializes next-generation semiconductor technologies, it could not only catch up to but potentially surpass the U.S. [5].