• The continuous AI integration of smart homes accelerates with the Matter protocol.

In the context of the modern era, smart homes are the AI applications that come second only to smartphones and smartwatches. As the penetration rate of smart home devices increases, more and more AI-enabled devices are permeating into human life, ushering in a large-scale era of personalization. The realization of smart homes not only requires smart appliances but also sensors and energy management systems. The deployment of AI will enhance recognition and control.

The diverse application scenarios of smart homes result in a wide variety of products. Despite the vast market size, there is an issue of product ecosystem fragmentation, leading to slow deployment. This can be addressed through the integration of the smart home market via the Matter protocol. As Matter facilitates communication between different devices through software protocols, the importance of software in devices will increase with the product’s AI capabilities, catering to the demands of edge AI applications.

• The RISC-V architecture is on the rise, and the form of MCUs with NPUs will continue to proliferate.

Although CPUs in MCUs are currently dominated by the Arm architecture, open-source RISC-V is gradually rising. In addition to its features such as customization, modularity, and cost-effectiveness, RISC-V is expected to become one of the advantages in smart home applications. It continues to gain support and application from many major manufacturers, expanding the ecosystem of the RISC-V architecture.

Because TinyML models are much smaller than general-purpose AI, they do not require a large amount of computational resources for deployment. This makes them suitable for IoT devices or smart homes that require large-scale deployment, with significant advantages in both technology and cost. Furthermore, with the diverse range of products in smart homes and the increasing demand for product functionality, the form of MCUs equipped with NPUs will become increasingly common as they adapt to the product’s uniqueness and evolve with AI integration.