半导体行业观察·2026-01-01 01:26Core Viewpoint - The embedded computing world is undergoing a transformation where AI is reshaping the architecture of MCUs, moving from traditional designs to those that natively support AI workloads while maintaining reliability and low power consumption [2][5]. Group 1: MCU Evolution - The integration of NPU in MCUs is driven by the need for real-time control and stability in embedded systems, particularly in industrial and automotive applications [3][4]. - NPU allows for "compute isolation," enabling AI inference to run independently from the main control tasks, thus preserving real-time performance [3][5]. - Current edge AI applications typically utilize lightweight neural network models, making hundreds of GOPS sufficient for processing, which contrasts with the high TOPS requirements in mobile and server environments [5]. Group 2: Major MCU Players' Strategies - TI focuses on deep integration of NPU capabilities in real-time control applications, enhancing safety and reliability in industrial and automotive scenarios [7][8]. - Infineon leverages the Arm ecosystem to create a low-power AI MCU platform, aiming to reduce development barriers for edge AI applications across various sectors [9][10]. - NXP emphasizes hardware scalability and a full-stack software approach with its eIQ Neutron NPU, targeting diverse neural network models while ensuring low power and real-time response [11][12]. - ST aims for high-performance edge visual applications with its self-developed NPU, pushing the boundaries of traditional MCU AI capabilities [13][14]. - Renesas combines high-performance cores with dedicated NPU and security features, focusing on reliable edge AIoT applications [15][16]. Group 3: New Storage Technologies - The introduction of NPU in MCUs necessitates a shift from traditional Flash storage to new storage technologies that can handle the demands of AI workloads and frequent updates [17][18]. - New storage solutions like MRAM, RRAM, PCM, and FRAM are emerging to address the limitations of Flash, offering advantages in reliability, speed, and endurance [21][22][25][28][30]. - MRAM is particularly suited for automotive and industrial applications due to its high reliability and endurance, with companies like NXP and Renesas leading in its adoption [22][23][24]. - RRAM offers benefits in speed and flexibility, making it a strong candidate for AI applications, with Infineon actively promoting its integration into next-generation MCUs [25][26][27]. - PCM provides high storage density and efficiency, suitable for complex embedded systems, with ST advocating for its use in advanced MCU designs [28][29]. Group 4: Future Implications - The dominance of Flash storage is being challenged as new storage technologies demonstrate superior performance and reliability for embedded systems [33]. - The integration of NPU and new storage technologies in MCUs represents a shift towards system-level optimization, enhancing overall performance and efficiency [33]. - The transformation in the MCU market presents structural opportunities for domestic manufacturers to innovate and compete against established international players [33].