With the rapid development of semiconductor technology, the Internet of Things (IoT) is increasingly becoming integrated with artificial intelligence (AI). In such context, the IoT- enabled equipment automation, fast data acquisition and remote control is working closely with AI to produce what is the so-called AI+IoT technology. The term of AIoT is literally the best combination of the IoT with artificial intelligence system. At present, such technology is being extensively used in multiple sectors from the industrial applications to people’s daily lives, with a view to providing more innovative solutions to various industries. With the AIoT technology, the deep learning is possible by devices collecting and analyzing a large amount of external data. The multi-level neural network training systems and outstanding computing power it boasts is helpful to behavior forecast and decision-making like human. All this, however, requires a large number of internal memories and CPU resources. The traditional clouding service mode takes huge cost as it has to port data to the cloud side for processing. On the other hand, the whole process is not free from data transmission latency and data security risk. The birth and growth of AIoT is aimed at providing better user experience in terms of security and convenience.
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As the digital economy grows rapidly, many enterprises are increasingly concerned about delay in data transmission, security and reliability of information. In response, the use of edge computing solution is on the rise. Unlike traditional computing mode, the edge computing is designed to make computing as close to data sources as possible to reduce latency and bandwidth usage, thus offering better transmission speed, lower operation costs and enhanced reliability and security. In the course of digital transformation, the MCU has played a big role in achieving Edge AI or Endpoint AI thanks to its various advantages such as low-power consumption, fast and low-cost development. We can see more and more MCUs engaged in the embedded machine learning (Embedded ML) and tiny machine learning (Tiny ML). The MCUs are equipped with varying performance to cater to different level of AI computation requirements.
The MCU-based AI system focuses on real-time decision-making and response speed because of its strengths in low-power consumption, low latency, low cost development and higher security. With the help of digital signal processor (DSP) and machine learning (ML), the system can be used for the classification, identification, prediction and inference judgment. It is found that many applications like sensor detection, motor vibration analysis and voice recognition, to name a few, are being widely used in industrial control, motor control and consumer electronics, among others. Besides, there are more high-end MCUs offering specific solutions to the complex computer vision and imaging applications, such as fingerprint analysis, facial recognition and collaborative robot. The continuous upgrading in AI algorithms has driven the MCU industry to expand its functions to adapt to the rapid growth of AIoT.
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With the growing demand of the device networking, the sector sees fast development, and a large number of human-machine interactions and high-speed communications keeps expanding, causing systems to be much more complex than before. Thus we are gradually turning to the artificial intelligence technology to attain a high level of smart management in this regard.
As an innovative leader in the 32-bit general-purpose MCU sector, ARTERY Tech offers up to 12 product lines covering low power line, value line, mainstream line, wireless BLE and high performance line, a total of nearly 200 parts. In addition to its 55nm advanced process, ARTERY MCU family all operate on ARM®-Cortex®-M4 or M0+ core and are equipped with a complete set of development tool kits, thus speeding up development and shortening production cycles. When it comes to the AIoT applications, AT32 MCUs developed by ARTERY stand out in four respects
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