（原标题：直击AI+教育场景核心痛点，清微智能联手灵书AI打造解决方案） 21世纪经济报道记者 申俊涵 实习生 方轶旻 北京报道 在数字经济与智能技术加速发展的背景下，职业教育正面临前所未有的转型契机。如何将人工智能技术有效融入教学、实训、评价等关键环节， 实现教学效率与人才培养质量的双重提升，成为当前教育信息化建设的核心议题。 "职业院校需要的是能够支撑上万人高并发、毫秒级低延迟响应，同时采购与使用成本可控的算力。传统方案高昂的成本和能耗，让很多学校望而 却步。"近日，在一场聚焦"人工智能+职业教育"深度融合的高端研讨会上，清微智能副总裁陈逸伦谈到当前职业教育领域AI落地的核心痛点。 据了解，清微智能成立于2018年，公司专注于国产原创的可重构芯片（Reconfigurable Processing Unit, RPU）的研发和产业应用，面向AI智算中 心、大模型、自动驾驶、智能制造等场景提供算力支持。 面对职业教育场景，师资缺口、素养落差、算力不均与协同低效构成了AI深度赋能教育的四大核心挑战。其中，作为底层驱动力的算力，其成 本、能耗和自主可控性更是关乎落地的关键。为此，清微智能与灵书AI联手打造的"A ...

Core Viewpoint - The article discusses the emergence of reconfigurable chips as a potential disruptor to the dominance of traditional GPU architectures, particularly those developed by Nvidia and Intel. Wang Bo, the founder of Qingwei Intelligent, represents a new approach that diverges from established paths in the AI chip market [1][2]. Group 1: Reconfigurable Chip Technology - Reconfigurable chips allow for dynamic configuration of computing resources, contrasting with GPUs that follow a fixed instruction-driven model. This flexibility enables multiple tasks to be performed without the need for extensive data transfer protocols [2][3]. - The architecture of reconfigurable chips moves away from the traditional von Neumann architecture, focusing on data flow-driven computation rather than instruction-driven processes [5][6]. Group 2: Market Position and Strategy - Wang Bo emphasizes that to compete with established players like Nvidia, companies must not follow the same paths, as this leads to inevitable failure. Instead, innovation and differentiation are crucial [2][24]. - Qingwei Intelligent aims to achieve a "5x cost-performance advantage" over competitors, which includes superior performance and lower costs, to attract customers and encourage them to switch from existing models [14][24]. Group 3: Product Development and Challenges - The company faced significant challenges in transitioning from laboratory technology to commercial products, requiring a focus on reliability, compatibility, and customer needs [8][21]. - After initial setbacks in consumer electronics, Qingwei Intelligent pivoted to focus on AI-centric chip development, leading to the successful launch of the TX81 chip, which has already garnered over 20,000 orders [11][14]. Group 4: Future Prospects and Innovations - The next generation of TX8 series chips will incorporate "3D storage" technology to enhance performance and achieve the promised cost-performance advantage [15][25]. - The company is also working on compatibility with existing ecosystems, including Nvidia's CUDA and open-source frameworks like Triton, to facilitate easier adoption of their technology by potential customers [28][29].

Core Viewpoint - The article discusses the emergence and significance of Reconfigurable Processing Units (RPU) as a key technology in the post-Moore's Law era, highlighting their potential in various fields such as artificial intelligence, edge computing, and data centers [2]. Global Perspective - The global development of reconfigurable chips is characterized by diverse technological paths, with significant advancements in applications across consumer electronics, aerospace, and data centers [4]. - Xilinx, a leader in the FPGA industry, introduced the Versal series adaptive computing acceleration platform in 2018, which integrates advanced reconfigurable computing IP, enhancing DSP processing capabilities and achieving low power consumption [4]. - Samsung integrates reconfigurable accelerators into its 8K TVs and Exynos SoCs, optimizing video decoding and AI image enhancement [5]. - Intel initiated a project in 2022 to integrate reconfigurable computing units into Xeon processors, resulting in a 40% reduction in power consumption per unit of computing power [5]. - PACT's dynamic reconfigurable processors have been applied in satellite and military communication systems, achieving high throughput and rapid reconfiguration times [6]. - SambaNova's RDU technology supports training models with up to 50 trillion parameters, outperforming NVIDIA's H100 in inference performance while significantly reducing total ownership costs [6]. Domestic Landscape - Qingwei Intelligent, a core representative of China's reconfigurable chip industry, has developed a comprehensive product matrix covering cloud, edge, and terminal scenarios since its establishment in 2018 [8]. - The TX8 series from Qingwei Intelligent achieves three times the energy efficiency of traditional GPU architectures, while the TX5 series excels in low-power image processing for applications like smart security [9]. - By 2024, Qingwei Intelligent's chip shipments are expected to exceed 20 million units, with deployments in various sectors including AI and energy [9]. Industry Trends and Challenges - Data flow-driven architecture is emerging as a mainstream trend, effectively overcoming storage limitations and enhancing computational efficiency in large-scale data processing [10]. - The industry is transitioning from a closed model dominated by single vendors to an open ecosystem, with companies like Samsung and Intel lowering development barriers and fostering collaboration [12]. - However, the fragmentation of reconfigurable compilation tools poses a significant challenge, hindering ecosystem development [12]. Application Expansion - Reconfigurable chips are rapidly penetrating fields such as smart security and industrial IoT, with significant energy efficiency improvements [13]. - SambaNova's reconfigurable chips deployed at Argonne National Laboratory provide superior performance and efficiency for large-scale data processing [13]. Summary and Outlook - Reconfigurable chips are reshaping the global computing landscape, with domestic companies establishing competitive advantages in niche markets [13]. - Future industry development should focus on creating unified programming standards, developing flexible and low-power architectures, and integrating reconfigurable technology with cutting-edge innovations [13].