Core Viewpoint - The article emphasizes that AI is transitioning from being a cloud-based privilege to becoming a standard feature in endpoint devices, with microcontrollers (MCUs) playing a crucial role in this shift [1][2]. Group 1: AI in Endpoint Devices - User demand is driving AI to "sink" into endpoint devices, as users prefer devices that can make decisions independently without relying on cloud processing [2]. - The AI chip market is projected to grow from $12 billion in 2019 to $43 billion by 2024, with edge AI being a significant growth driver [2]. Group 2: MCU Industry Transformation - The MCU industry is undergoing a transformation as AI capabilities are increasingly integrated at the hardware level, particularly through the integration of neural processing units (NPUs) [1][2]. - Major MCU manufacturers are moving beyond merely adding AI features in software toolkits to integrating NPUs into their hardware, marking a new era in edge intelligence [2]. Group 3: Strategies of Major MCU Players - STMicroelectronics has developed its own NPU, Neural-ART, and launched the STM32N6, which features high performance and significant AI capabilities [5][6][10]. - NXP has introduced the eIQ Neutron NPU, which supports various neural network types and has been integrated into its i.MX RT700 and S32K5 MCUs [11][13][14]. - Infineon is leveraging the Arm Ethos-U55 NPU in its PSOC Edge series, focusing on reducing AI development barriers [18][19]. - Texas Instruments has introduced the TMS320F28P55x C2000 series, the first real-time control MCU with an integrated NPU, enhancing fault detection and reducing latency [20]. - Renesas is optimizing its RA8 series MCUs for AI without an NPU, focusing on cost-effectiveness and simplicity [22]. - Silicon Labs is targeting low-power AI for IoT applications with its xG26 series, emphasizing energy efficiency [23][24]. Group 4: Domestic MCU Manufacturers - Domestic players like Guoxin Technology and Zhaoyi Innovation are developing AI-capable MCUs, with Guoxin's CCR4001S featuring a self-developed NPU for edge AI applications [25][27]. - Zhaoyi Innovation's GD32G5 series MCU is designed for AI algorithm processing, while Chengpu Microelectronics is integrating TinyML capabilities for offline voice recognition [27][28]. Group 5: Future Trends in MCU and AI - The integration of AI into MCUs is becoming inevitable, with AI expected to be a built-in capability rather than an add-on feature [29]. - The market demands for AI MCUs vary across segments, with consumer electronics prioritizing cost and ease of deployment, while automotive and industrial sectors emphasize safety and reliability [29][30]. - The shift towards mixed CPU + NPU architectures is anticipated to redefine product definitions and impact the semiconductor supply chain [30].