From high-performance cores to dual-core designs, MCUS are attacking edge AI

With the development of artificial intelligence, its application in various fields is more and more extensive, and edge artificial intelligence has become a hot spot. Edge artificial intelligence refers to the application of artificial intelligence algorithms to edge devices, such as embedded systems, sensors, intelligent terminals, etc., to achieve real-time response, low power consumption, high security and other advantages. High-performance core and dual-core design are one of the important technical means for MCU to attack edge AI.

Ⅰ, high-performance core

High performance core refers to the core processor in MCU chip, and its performance determines the processing power and running speed of MCU chip. In traditional MCU design, the core usually uses a relatively simple 8-bit or 16-bit processor, which is mainly to save cost and power consumption, and is suitable for some simple application scenarios.

However, with the development of edge artificial intelligence, higher requirements are put forward for the processing power and running speed of MCU chips, so MCU chips need to adopt higher performance cores. At present, many MCU chips using 32-bit or 64-bit high-performance cores have appeared on the market, such as STMicroelectronics STM32 series, Renesas RX series, NXP Kinetis series and so on.

These high-performance core MCU chips have higher computing speed, stronger computing power and more complex instruction sets, which can better support the operation and processing of edge artificial intelligence algorithms. At the same time, these chips also have more abundant peripheral interfaces and higher integration, which can meet the needs of more complex applications.

Ⅱ. Dual-core design

Dual-core design refers to integrating two or more cores into the same MCU chip and working together through a certain communication mechanism to improve the processing power and operation efficiency of the MCU chip. The dual-core design allows different tasks to be assigned to run on different cores, allowing for better parallel computing and resource utilization.

In edge AI applications, dual-core design can assign AI algorithms and real-time control algorithms to run on different cores, thus achieving a balance between edge computing and real-time response. For example, AI algorithms can run on high-performance cores, and real-time control algorithms can run on low-power cores, enabling efficient operation of edge AI.

At present, there are many MCU chips using dual-core design on the market, such as STMicroelectronics STM32H7 series, NXP i.MX RT series, Renesas RXv3 series and so on. These chips use different dual-core designs, such as ARM Cortex-M7 and Cortex-M4 dual-core designs, Cortex-M33 and Cortex-M0+ dual-core designs, to meet different application requirements.

Ⅲ. Edge artificial intelligence applications

With the application of high-performance core and dual-core design, MCU chips are more and more widely used in the field of edge artificial intelligence. Edge AI applications usually need to meet the following requirements:

(1) Real-time response: Edge artificial intelligence applications need to complete calculations and decisions in a relatively short time, so as to achieve real-time response functions.

(2) Low power consumption: Edge devices are usually battery powered or power limited, so edge AI applications need to have low power consumption.

(3) High security: Edge devices usually carry important data and sensitive information, so edge AI applications need to have high security to prevent data leaks and attacks.

(4) Efficient computing: Edge artificial intelligence applications need to have efficient computing power and processing power, and can realize the operation and processing of complex algorithms and models.

Based on these requirements, the edge AI application of MCU chips mainly includes the following aspects:

(1) Sensor data processing: The MCU chip can collect various data through the sensor, and conduct real-time processing and analysis, such as temperature, humidity, pressure, sound, image, etc.

(2) Data classification and recognition: MCU chips can classify and recognize data through machine learning algorithms, such as speech recognition, image recognition, gesture recognition, etc.

(3) Intelligent control and optimization: MCU chips can achieve intelligent control and optimization through artificial intelligence algorithms, such as smart home, industrial automation, intelligent transportation, etc.

(4) Security monitoring and early warning: MCU chips can achieve security monitoring and early warning through artificial intelligence algorithms, such as intrusion detection, smoke alarm, abnormal behavior detection, etc.

In short, MCU chips, through technical means such as high-performance core and dual-core design, are attacking the field of edge artificial intelligence, providing more efficient, safe and intelligent solutions for various application scenarios.