With the development of cloud computing and the Internet of Things, edge AI technology combining the two has begun to receive more and more attention in recent years. In simple terms, edge AI refers to AI computing implemented on edge devices. Historically, most cutting-edge AI processes have been implemented in the cloud because they require a lot of computing power. But as the rate at which data grows, the need for local data storage becomes more urgent, and a lot of data manipulation is gradually moving on-premise, which is why AI is widely adopted at the edge.
Since edge AI refers to deploying and running AI models on edge devices, rather than transmitting data to a central server for processing. This approach has the advantages of low latency, high response speed, privacy protection, and reduced data transmission costs. As the main control chip of electronic devices, microcontrollers (MCUs) play an important role in the development of edge AI and have a wide range of applications.
Application of MCUs in edge AI
Industrial Automation:
The application of edge AI in industrial automation is mainly focused on predictive maintenance and machine vision. By monitoring the performance of industrial equipment and systems in real time, edge AI can proactively identify potential failures, reducing downtime and increasing productivity. For example, ST provides a complete solution for industrial predictive maintenance through the NanoEdge Studio model creation tool, based on the STM32 hardware platform.
Smart Home:
In the smart home space, edge AI can enable a more intelligent and personalized home experience. By analyzing and processing data collected by smart home devices and sensors in real time, edge AI can provide real-time environmental monitoring and automated control to improve home comfort and safety.
Figure:A New Opportunity for MCU
Smart Factory:
The application of edge AI in smart factories can help enterprises achieve smarter, more efficient, and more sustainable production. By processing data locally on the device, edge AI can reduce data transmission and response times, improve data security, and maintain operational stability in the event of an unreliable or unstable network.
Smart Transportation:
The application of edge AI in intelligent transportation can achieve a more intelligent, efficient, and safe transportation system. By analyzing and processing data collected by devices and sensors such as traffic lights, road condition monitoring, and vehicle identification in real time, edge AI can provide real-time traffic monitoring and intelligent traffic control.
In addition, MCUs also have many advantages in edge AI, such as: edge AI can process data locally on the device, reducing the transmission time of data, thereby achieving low latency and high response speed, reducing transmission costs, and improving data reliability.
Edge AI in the new direction of MCU development will bring more efficient, secure, and intelligent IoT and intelligent application experience. With the continuous progress of technology and the expansion of application scenarios, edge AI will play an increasingly important role in the MCU field.
