Author: Fang Yuan
Smart speakers can understand voice commands, air conditioners support remote control, cameras identify people and pets, and robot vacuums have already acquired the ability to autonomously plan cleaning routes. However, for ordinary households, true whole-home intelligence has not yet been fully popularized. Devices from different brands often operate within independent control systems, forcing users to switch between multiple apps. The so-called smart linkage frequently remains limited to simple operations based on preset conditions.
The core issue is that device connectivity does not equate to true device intelligence. Over the past decade, the smart home industry primarily focused on solving how devices access networks and accept remote controls. The next phase requires addressing how devices understand environments, recognize user needs, and achieve coordination across different equipment.
Recently, Apple presented its new answer. On October 13, Apple officially launched three home products: the HomeView smart home hub with a 7-inch display, the second-generation HomePod mini, and the fourth-generation Apple TV 4K. Unlike previous hardware-only updates, this time Apple positioned Siri AI and Apple Intelligence as key selling points, aiming to integrate entertainment, communication, personal information, and smart device control through the home hub. This strategy seeks to transform the voice assistant from a simple command-execution tool into an intelligent interaction gateway in home scenarios.
As Apple intensifies its push into smart homes, companies like Huawei, Xiaomi, and Amazon are also advancing their respective home ecosystems. Behind this competition, what warrants attention from the semiconductor industry is no longer just the sales volume of terminal products like smart speakers and screens, but the changing chip architectures supporting the entire home intelligence system.
The Next Phase of Smart Homes: Achieving True Device Interoperability
Smart homes are not a new concept. From early smart lighting and locks to smart speakers and robot vacuums, and now to whole-home intelligence, the industry has undergone multiple rounds of product iteration.However, a long-standing problem remains unresolved: why do devices from different brands and categories struggle to work together seamlessly? This stems from the early development path of the smart home industry.
In the initial market stage, manufacturers focused on differentiating single-product features. Air conditioner companies developed proprietary control systems, lighting firms built independent device management platforms, and smart speaker vendors established ecosystems around voice assistants. For enterprises, controlling device entry points, user data, and software platforms created competitive barriers. However, from the consumer perspective, compatibility issues between different systems arose. Users might purchase air conditioners, lights, locks, and cameras from various brands, only to find they require multiple apps, and some devices cannot connect to the same control platform. Even when linkage is possible, it often requires pre-setting fixed conditions, lacking the capability to understand complex home scenarios. Therefore, the first priority for the next phase of smart homes is not adding more features, but establishing a more unified device connection and management system.
Matter serves as a crucial technical foundation for this direction. As a smart home interoperability protocol promoted by the Connectivity Standards Alliance (CSA), CSA officially released Matter version 1.6 in June, further enhancing capabilities such as device provisioning, cross-ecosystem management, and contextual control. Specifically, NFC provisioning simplifies the installation process for certain devices, while the Joint Fabric mechanism supports multiple authorized controllers managing devices jointly. This indicates that Matter's evolution goes beyond enabling mutual device recognition; it also attempts to improve the actual experience when multiple smart home platforms manage devices together.
The domestic market is progressing along similar lines. Huawei HarmonyOS Smart Home continues to strengthen cross-device and cross-space scenario linkage, integrating lighting, air conditioning, security, and other devices into the home environment through central control panels, voice interaction, sensors, and intelligent control systems. Xiaomi relies on smartphones, smart speakers, routers, and a vast array of IoT terminals to continuously expand its smart home product matrix.
From Apple's home hub to Huawei's HarmonyOS Smart Home and Xiaomi's smart ecosystem, although each company adopts different technical paths and business models, the core competitive direction is converging: whoever enables more devices to connect stably has a greater chance of becoming the central gateway for home intelligent services.
This also signifies that the value of the smart home industry is shifting from single hardware components to system-level solutions. For semiconductor companies, while an increase in device numbers drives chip demand, what deserves more attention is that as device coordination improves, requirements for chip performance, power consumption, communication capabilities, and software-hardware adaptation are rising simultaneously.
From Connectivity to AI Services: Three Types of Chips Deserve Attention
If the previous phase of smart homes mainly addressed device connectivity and remote control, then after AI enters the home,industry demand for chips is no longer limited to communication and basic control.A complete smart home system must at least perform three fundamental tasks: connecting different devices, sensing the home environment, and analyzing collected information to make decisions. Corresponding to the semiconductor supply chain, MCUs and wireless connectivity chips, AI processors, and sensors constitute the three types of foundational hardware worth watching.
First are the chips responsible for connectivity and control.Products such as smart lights, door/window sensors, smart switches, and temperature/humidity monitors typically do not require powerful computing capabilities but need long-term stable operation with minimized power consumption and manufacturing costs. Consequently, MCUs remain indispensable core components for many smart home terminals. MCUs are primarily responsible for executing preset control logic, reading sensor data, managing peripheral devices, and handling basic task scheduling. With increasing demands for device connectivity, some products integrate Wi-Fi, Bluetooth, Thread, and other wireless communication capabilities, forming higher-integration wireless SoCs.
A typical example in this area is Espressif Systems, a wireless connectivity chip manufacturer. Its product portfolio covers technologies including Wi-Fi, Bluetooth, Thread, and Zigbee, offering Matter-related chip solutions. In the first three quarters of 2026, the company achieved revenue of CNY 2.37 billion, a year-on-year increase of 23.94%. The Q3 report noted that demand in the smart home industry remained generally stable, with the expansion of IoT application scenarios serving as a significant backdrop for performance growth. On the product front, the ESP32-S31 launched this year entered mass production in July. This chip adopts a dual-core RISC-V architecture, integrates Wi-Fi 6, Bluetooth 5.4, Thread, and Zigbee connectivity, and adds hardware support for image processing and human-machine interaction, reflecting the trend of wireless connectivity chips extending toward more complex terminal applications.
Traditional smart home control logic is relatively simple. For instance, if the temperature exceeds a set value, the air conditioner starts; if human activity is detected, the lights turn on. Such tasks can be completed via MCUs and preset rules. However, when devices need to understand natural language, analyze camera feeds, identify abnormal behaviors, or determine home scenarios based on data from multiple sensors, computational complexity increases significantly. At this point, AI SoCs, NPUs, and dedicated coprocessors begin to play a role. Taking home cameras as an example, traditional products mainly handle video capture, encoding, and transmission. After incorporating AI computing capabilities, devices can locally perform tasks such as partial human detection, pet recognition, and abnormal activity analysis, reducing invalid video uploads and cloud computing pressure. Smart hubs can combine user commands, device status, and environmental information to execute more complex task scheduling.
Rockchip Electronics is a notable domestic chip company in this field. Its layout includes product lines covering AIoT application processors, vision processors, and coprocessors. In May, it launched the new mid-range AIoT processor RK3572, integrating an NPU with 4 TOPS of computing power, targeting applications such as smart cameras, video analysis, and multimedia terminals. The RK182X series coprocessor has also entered mass production and is combined with the RK3588 in AI TVs, supporting on-device multimodal models, screen recognition analysis, and offline voice interaction. Additionally, vision processors like RV1126B and RV1106 are primarily designed for smart cameras and machine vision scenarios. It is evident that Rockchip is expanding local AI computing capabilities for terminal devices through combinations of main control chips and coprocessors.
The third category consists of sensors responsible for perceiving the environment. If communication chips solve how information is transmitted between devices, and AI processors solve how information is analyzed, then sensors serve as the entry point for the entire system to acquire external information. Traditional smart homes relied mainly on active user input or triggering preset actions via single parameters like temperature and humidity. However, true scenario intelligence requires the system to understand multiple changes within the home environment.
For example, determining whether a room is occupied using only standard infrared motion sensors may have limitations. Assessing whether an elderly person has fallen, whether a user is sleeping, or if there is abnormal activity in a room may require comprehensive judgment combining millimeter-wave radar, infrared sensors, cameras, and other environmental data. This opens up new application spaces for MEMS sensors, environmental sensors, image sensors, and millimeter-wave radar-related chips. Temperature, humidity, light, and air quality sensors can be used for environmental monitoring and automatic appliance adjustment; image sensors capture visual information; and millimeter-wave radar obtains target distance, speed, and motion state information via electromagnetic wave reflection, offering value in non-contact presence detection, fall detection, and similar scenarios.
Taking some scenario solutions in Huawei HarmonyOS Smart Home as examples, perception technologies like millimeter-wave radar have already been applied to functions such as auxiliary sleep state detection and fall event detection, linking with lighting, air conditioning, and other devices. This shift implies that sensors are no longer merely accessory components of specific appliances but are gradually becoming a critical foundation for whole-home intelligent systems to acquire environmental information.
It must be emphasized that sensors themselves do not directly perform complex scenario judgments. They are responsible for data acquisition, while algorithms, processors, and control systems participate subsequently. Therefore, future smart home competition will not involve upgrading a single type of sensor, but rather the synergy between diverse perception technologies and computing platforms.
AI Entering the Home: Where Are the Opportunities for Domestic Chips?
Introducing AI into smart homes does not mean all computations must reside in the cloud. In fact, home scenarios have specific requirements regarding response speed, privacy protection, and network stability, making edge-side AI a significant development direction.
Take home cameras as an example. If every frame analysis requires uploading to the cloud, it not only increases network bandwidth and server computing costs but may also raise privacy concerns. For devices like locks, security systems, and environmental monitors, some tasks need to be completed as promptly as possible. Edge computing allows processing part of the data locally on the device or home gateway, reducing reliance on continuous network connections and limiting the scope of sensitive information transmission. Meanwhile, the cloud can still handle complex model inference, knowledge updates, and cross-device data management.
The design of Apple's HomeView reflects this approach. Equipped with the A18 chip, it uses Secure Enclaves to isolate camera and microphone data, while allowing Siri requests to utilize on-device processing or Private Cloud Compute depending on the task type. This indicates that even Apple, which emphasizes personal privacy, has not confined all AI capabilities to the local device but is exploring the combination of edge hardware and cloud computing. For domestic semiconductor companies, this change brings new development opportunities.
China possesses a relatively complete smart home terminal manufacturing system, with numerous local brands and supply chain enterprises participating in areas ranging from smart lighting and appliance controllers to cameras, smart locks, and home gateways. Continuous upgrades in terminal products provide broad application entry points for MCU, wireless connectivity chip, AI processor, and sensor companies. Secondly, the development of edge AI offers product upgrade space for chip companies. Previously, some smart home products had low computing requirements, with competition focusing more on cost, power consumption, and basic functions. As local voice recognition, visual analysis, and complex scenario control gradually materialize, chip companies can enhance product added value by increasing integration, optimizing energy efficiency, and developing dedicated AI acceleration modules.
The layouts of companies like Rockchip and Espressif exemplify these industrial shifts. Simultaneously, the domestic smart home appliance MCU and smart controller industries continue to develop, providing relevant enterprises opportunities to benefit from appliance intelligence and control system upgrades.
However, it is necessary to recognize that smart home market opportunities will not automatically translate into performance growth for chip companies. Consumers' willingness to pay for whole-home intelligence is still constrained by cost and actual experience. For ordinary families, if a smart system requires replacing many appliances, installing complex central control devices, and entails high subsequent maintenance costs, rapid market penetration becomes difficult. Only when intelligence genuinely improves the living experience will consumers be willing to pay continuously.
Secondly, the smart home industry has distinct ecological characteristics. Even if chip companies possess high-performance products, lacking mature software development environments, protocol adaptation capabilities, and customer support systems makes it difficult to quickly enter mainstream terminal supply chains. Standards like Matter lower the barrier to interoperability but cannot eliminate differences among manufacturers in product design, system adaptation, and service experience. Another easily overlooked issue is the long lifecycle of home devices. Consumers typically hope to use an air conditioner or refrigerator for many years. If smart home platforms cannot provide continuous software updates, stable device compatibility, and long-term security maintenance, even if the hardware itself has AI capabilities, it will be hard to form a truly reliable home intelligent system.
Conclusion
For the semiconductor industry, the next round of opportunities in smart homes may lie not in a single hit appliance, but in the hardware architecture upgrade of the entire home system. The tipping point for smart homes may be arriving, but it will not be driven solely by one AI speaker or one home hub. What truly determines the pace of industrial development is whether underlying chips, communication standards, software ecosystems, and terminal demands can form a synergistic force. For domestic semiconductor companies, this represents both a new market opportunity and a test of upgrading from single-component suppliers to system-level solution providers.