Embedded itself is a general term of technology, which is a special computer system centered on application, based on computer technology, whose software and hardware can be tailored to meet the strict requirements of application system on function, reliability, cost, volume, power consumption and so on. Simply put, just like the mobile phones we use, they have various functions, such as tailorability, removable batteries, low energy consumption, etc. This is the application of embedded technology. Just like mobile phones, mobile phones apply embedded technology, and all nodes of smart homes are embedded technology. Maybe the air conditioners, washing machines, televisions, and microwave ovens in the home are all completed by embedded technology, which are collectively referred to as smart devices, The so-called intelligent devices belong to the category of artificial intelligence.

The embedded AI market has great potential in the vertical fields of retail, transportation and automation, manufacturing, agriculture and other industries. The main factor driving the market is the increasing number of applications of embedded artificial intelligence technology in various end-user vertical fields, especially the improvement of end-user services. Of course, the embedded AI market will also be popularized by the improvement of IT infrastructure, smart phones and smart wearable devices. Among them, natural language processing (NLP) application market accounts for a large part of the embedded artificial intelligence market. With the continuous improvement of natural language processing technology, which drives the growth of consumer services, there are also fields such as automotive information communication and entertainment systems, embedded artificial intelligence robots and smart phones supporting embedded artificial intelligence.

In the process of AI commercialization, to realize the implementation of technology, we not only need excellent algorithm model and reliable hardware support, but also need to organically combine AI technology and hardware environment, and then apply it to specific practical scenarios. If the future is an intelligent world, every terminal should be intelligent, which must rely on embedded AI technology.

Embedded refers to a special computer system that can be built into equipment or devices. Generally speaking, devices with digital interfaces have embedded systems, such as mobile phones, on-board computers, smart watches and so on. Embedded AI is a technology that enables AI algorithms to run on terminal devices. Generally speaking, embedded AI has natural advantages for scenes with high real-time processing, such as unmanned aerial vehicles, intelligent cars, industrial machinery, electrical appliances, security checks and other scenes, which have been more and more applied.

If you look closely, you will find that many AI technologies around us are realized through embedded systems, such as smart speakers and floor sweeping robots used at home, or auxiliary driving we use in travel, including various smart screens in the office, smart conference systems, etc., which are the combination of AI technology and embedded systems. From this perspective, the future digital era is bound to be the world of AI embedded systems, and what we need to learn is how to realize the underlying implementation and optimization technology of a variety of machine learning algorithms in the field of AI on embedded systems.

For a long time, the artificial intelligence we talked about may be realized based on PC or even server, so performance is the focus of attention in the past. But for embedded systems, in addition to conventional computing and storage, there are high requirements for power consumption, volume, computing power and storage capacity, while embedded systems in different fields are limited by various application requirements, and it is difficult to meet all conditions. Therefore, AI embedded system also has its own unique application environment, which requires differentiated solutions.

In recent years, research has found that there are a lot of redundant operations and parameters in many neural network architectures. Through the simplification of operations and architecture tailoring, the requirements of neural network for computing performance and memory can be greatly reduced, making it possible to implement it on embedded systems. Therefore, the book also focuses on the implementation of machine learning algorithms based on statistical learning and neural network on embedded systems, and introduces specific optimization methods through algorithms and routines.

Future embedded AI needs AR, and future ar also needs embedded AI. AR can be compared to the eye of embedded AI. The virtual world created for robot learning is itself virtual reality. In addition, if people want to enter the virtual environment to train robots, more other technologies are needed. Looking forward to the future, with the maturity of embedded artificial intelligence, Internet of things, vr/ar, 5g and other technologies, it will drive the 30-year prosperity of a new wave of semiconductor industry, including four chips: memory, central processing unit, communication and sensor. The demand for various new product application chips is increasing, and China's huge market advantage in semiconductors will play a key role in the world.

————Transferred from 21IC electronic network

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