Investment Rating - Industry Rating: Outperform the Market (Maintained Rating) [1] Core Insights - The semiconductor industry is expected to continue its optimistic growth trajectory in 2025, driven by AI demand in downstream applications. The report highlights the ongoing push for domestic substitution and the positive performance forecasts for various segments in Q2 and Q3 [6][12]. - The introduction of Huawei's UCM technology is anticipated to significantly boost SSD demand by enhancing AI inference efficiency through innovative data management strategies [10][21]. Summary by Sections Semiconductor Market Overview - In July, major chip manufacturers experienced an increase in delivery times, with prices for storage, analog, power, and passive components showing signs of recovery. The report anticipates a continued upward trend in prices and improved order conditions in the distribution channel for Q3 [5][11][22]. Key Technologies and Innovations - The "Storage Computing" paradigm, initiated by Huawei's UCM, is expected to revolutionize the storage industry by optimizing AI inference processes and reducing reliance on HBM. This innovation is projected to enhance the efficiency of enterprise-level SSD products [10][17][21]. Market Segments and Recommendations - The report suggests focusing on sectors such as storage, power, foundry, ASIC, and SoC for their performance elasticity. It also emphasizes the importance of equipment materials and the domestic substitution of computing chips [6][12]. - Specific companies to watch include Jiangbolong, Zhaoyi Innovation, and Huahong Semiconductor among others, which are positioned to benefit from the anticipated market trends [7].

Core Viewpoint - Normal Computing has successfully developed the world's first thermodynamic computing chip, CN101, designed for AI and HPC data centers, achieving unprecedented computational efficiency compared to traditional silicon methods [2][4]. Group 1: Chip Technology and Efficiency - The CN101 chip utilizes thermodynamics and other physical principles, diverging from traditional silicon computing, and is closer to quantum and probabilistic computing [2][4]. - The chip can achieve up to 1000 times energy efficiency in AI training tasks, significantly enhancing performance within existing energy budgets [4][5]. - Normal Computing's approach leverages randomness and noise, which are typically detrimental in standard electronics, to solve problems, thus expanding the algorithmic space for applications ranging from scientific computing to AI [2][4]. Group 2: Applications and Future Roadmap - CN101 is specifically designed for linear algebra and matrix operations, crucial for engineering, scientific computing, and optimization tasks [4][5]. - The roadmap includes the release of CN201 in 2026 for high-resolution diffusion models and CN301 in late 2027 or early 2028 for advanced video propagation models [6][7]. - The successful tape-out of CN101 marks a historic moment for thermodynamic computing, paving the way for future developments in AI workloads [6][7].