三维存算一体3D-CIM，破解AI芯片产业困局！

Core Viewpoint - The AI industry is experiencing explosive growth in computing power demand, with a projected global smart computing power gap of 35% by 2025, highlighting the critical role of AI chips in the technology competition landscape [2][3]. Group 1: AI Chip Market Dynamics - The demand for AI computing power is driven by large model parameters expanding at a rate of 10 times per year, particularly in key applications like autonomous driving and smart healthcare [2]. - The domestic AI chip industry faces challenges such as technological bottlenecks, limitations of traditional architectures, and insufficient ecosystems, necessitating the development of a self-controlled AI chip ecosystem [2][3]. Group 2: RISC-V Architecture and Its Significance - The RISC-V architecture is emerging as a crucial breakthrough due to its open-source and scalable nature, enabling the construction of a self-controlled hardware and software ecosystem for domestic AI chips [2][3]. - The RISC-V computing architecture is being integrated into high-performance computing, enhancing inference efficiency and energy efficiency compared to traditional architectures [2][3]. Group 3: Industry Forum and Standardization - The RISC-V Storage-Compute Integration Industry Forum was held on September 9, 2025, marking a significant step in breaking traditional computing architecture bottlenecks and establishing a self-controlled AI chip ecosystem in China [3][5]. - The forum aimed to establish a standardized roadmap for "RISC-V + Storage-Compute Integration" technology to support domestic chips in addressing large model computing challenges [5]. Group 4: Technical Challenges and Solutions - The AI chip industry faces three major challenges: hardware bottlenecks, ecological barriers, and architectural limitations, with the need for innovative technologies to address the growing computing power demands [9][10]. - The report emphasizes the importance of a new architecture and an open-source self-controlled ecosystem to overcome these challenges, proposing a three-dimensional storage-compute integration (3D-CIM) approach [10][12]. Group 5: Innovations in Storage-Compute Integration - The 3D-CIM approach integrates SRAM storage and computing, significantly improving computing efficiency and energy density, with potential energy efficiency improvements of 5-10 times compared to traditional digital circuits [12][15]. - The architecture supports various data formats and is designed to meet the full range of AI model requirements, although it still faces challenges in computational completeness and software ecosystem development [13][14]. Group 6: Strategic Implications and Future Outlook - The 3D-CIM technology is expected to empower the AI industry across various applications, from edge AI models to cloud computing, and aims to provide a self-controlled chip solution for China's AI model industry [16][18]. - The integration of RISC-V architecture with storage-compute technology is seen as a pathway to overcome international technological restrictions and enhance domestic chip capabilities [17][18].