此次融资汇聚国家产业资本、战略资本与头部市场化基金，既是对铭芯启睿技术创新的高度认可， 也是对新型存储与存算产业发展潜力的充分看好。 铭芯启睿成立于2024年5月，致力于以先进工艺节点阻变存储器（RRAM）为基础，结合存算融合 及先进封装技术，解决"内存墙"瓶颈，面向服务消费、工业与数据中心领域，提供AI高性能"感- 存-算"一体化解决方案及定制化存储IP/芯片产品。 如果您希望可以时常见面，欢迎标星收藏哦~ 近日，铭芯启睿完成超亿元人民币Pre-A轮融资。本轮融资由国开科创、联想创投领投，中芯聚 源、顺禧基金、恒裕投资跟投，老股东中科创星、小米战投持续加码，募集资金将用于RRAM核心 技术研究和人才团队扩充，助力推动RRAM技术产品规模化量产，加速存算技术的落地应用。 中科创星表示： "下一代算力架构的突破，必须始于底层。RRAM及存算一体技术，正是从材料与 器件层面重构计算的深刻变革，尤其在3D器件及3D堆叠技术进一步成熟后，RRAM的非易失低功 耗特性会更加凸显。我们持续加注铭芯启睿，正是看重团队在新型存储领域的前沿洞察、扎实积 累，以及从技术到产品的快速工程化能力——这体现了硬科技创新的本质：在源头处 ...

北京铭芯启睿科技有限公司成立于2024年5月，致力于以先进工艺节点阻变存储器（RRAM）为基础，结合存算融合及先进封装技术，解决"内存墙"瓶颈， 面向服务消费、工业与数据中心领域，提供AI高性能"感-存-算"一体化解决方案及定制化存储IP/芯片产品。 经过一年多的快速发展，铭芯启睿在探索高密度存储商业化落地的过程中成果显著。目前，公司已与多家上下游企业建立合作，围绕存储工艺制造技术展开 联合攻关，并深度绑定多家龙头企业客户，共同定义更符合市场需求的优质产品，且已顺利完成产品工程批验证流片。同时，铭芯启睿将加快存算技术产品 的研发，为客户提供更高价值的人工智能存算方案。 来源：猎云网 铭芯启睿联合创始人、董事长刘琦教授表示："未来，我们将持续深耕新型存储与存算技术赛道，聚力创新，以卓越的技术和产品回报信任、服务客户。" 近日，感存算一体化技术产品及解决方案提供商铭芯启睿完成超亿元Pre-A轮融资，本轮融资由国开科创、联想创投领投，中芯聚源、顺禧基金、恒裕投资 跟投，老股东中科创星、小米战投持续加码。 据了解，本轮融资将用于RRAM核心技术研究和人才团队扩充，助力推动RRAM技术产品规模化量产，加速存算技术的落地 ...

Core Insights - The research team from Peking University has achieved a breakthrough in high-precision, scalable analog matrix equation solving, published in Nature Electronics, marking a significant advancement in analog computing technology [1][2] - This innovation demonstrates that analog computing can efficiently and accurately address core computational problems in modern science and engineering, potentially disrupting the long-standing dominance of digital computing [2][3] Group 1: Key Innovations - The first key innovation is the use of resistive random-access memory (RRAM), which allows for precise control of resistance states and retains data without power, enabling it to function as both a memory and a computing unit [4] - The second key innovation stems from a foundational discovery in 2019, where the team designed an analog circuit capable of solving matrix equations in a single step, significantly compressing traditional iterative algorithms [5] - The third key innovation is the "bit slicing" technique, which breaks down 24-bit precision into multiple 3-bit segments for processing, allowing for a more sophisticated and efficient analog computation [5] Group 2: Practical Implications - The breakthrough allows for solving matrix equations with 24-bit precision in just a few iterations, drastically reducing the computational steps required for complex tasks, such as 6G signal detection [7] - In the AI field, this advancement could alleviate the "computational bottleneck" faced by large models, enabling faster and more efficient training processes [7] - The technology also addresses critical challenges in 6G communication, enhancing signal detection capabilities while significantly reducing energy consumption [8]