Core Viewpoint - The article discusses the innovative RRAM technology developed by "Mingxin Qirui," which integrates storage and computing to significantly enhance AI computing efficiency. The company recently completed nearly 100 million yuan in angel financing, led by Jin Qiu Fund, with participation from major strategic and financial investors like Lenovo Ventures and Xiaomi Investment [1][4]. Group 1: Company Overview - "Mingxin Qirui" was established in May 2024 and focuses on developing new RRAM storage and AI computing technologies to overcome traditional computing architecture limitations [1]. - The company has a strong foundation in intellectual property, with over 200 patents and chip design IP derived from the research team at the Chinese Academy of Sciences, which has over 20 years of systematic research in semiconductor storage [3]. Group 2: Technology Advantages - RRAM technology allows for the integration of storage and computation, addressing the "memory wall" bottleneck in traditional computing architectures, especially in AI applications that require extensive matrix operations [1][2]. - RRAM consumes significantly less energy compared to traditional storage, has the potential for higher storage density due to its ability to modulate multiple resistance states, and is expected to play a crucial role in AI computing [2]. Group 3: Market Position and Partnerships - The company is rapidly advancing its productization and commercialization efforts, having signed contracts worth several million yuan for embedded IP and strategic cooperation agreements for independent RRAM chips [4]. - Major industry players, including TSMC, Samsung, Micron, and SK Hynix, are also exploring RRAM technology, indicating a competitive landscape [3]. Group 4: Investment Insights - Investors view RRAM as a promising technology for AI computing, with Jin Qiu Fund highlighting its low power consumption, fast read/write capabilities, and high density as key advantages [5][6]. - Lenovo Ventures emphasizes the potential of RRAM to significantly reduce AI operational costs and enhance computing efficiency, aligning with the evolving demands of the AI landscape [6].