EdgeThe trend of AI intelligent development is a combination of marginal computing and artificial intelligence.Edge AI refers to a technology that directly deploys AI applications in equipment in the physical world.It allows calculations near the location of the actual creation of data, rather than relying on the development process of the AI development of centralized cloud computing facilities or the edge of the data center of different places.
edgeAI can be widely used in various industries and scenarios, including manufacturing, medical care, energy, retail, transportation, etc.
For example, in intelligent manufacturing, edgeAI can process data on the production line in real time to achieve rapid decision -making and optimization; in intelligent transportation, edge AI can process traffic signal lights and sensor data to achieve intelligent traffic control and security monitoring.
EdgeHow does AI develop? This should start with the earliest cloud computing.From the beginning of the 2000s to 2005, the rise of cloud computing, cloud computing, as a form of distributed computing, decomposed huge data computing processing procedures into countless applets, and processed and analyzed by systems composed of multiple servers.At this stage, cloud computing solves the problem of mission distribution and calculation results, which provides possibilities for massive data processing.
ArriveAfter the 2010s, the Internet of Things IoT and 4G5G wireless networks became popular. With the rapid increase of IoT devices, the amount of data generated by network edge devices rapidly expanded, reaching the Zebo (ZB) level.The popularization of 4G and 5G wireless networks further promoted the data transmission capacity of marginal devices, but also challenged data processing capabilities, network transmission bandwidth load capacity, personal privacy protection, etc.
ArriveIn the middle and late 2010, in the face of the limitations of cloud computing in edge data processing, edge computing was proposed as a new type of computing mode.Edge computing emphasizes the capture and processing data near the data source or terminal, and the data transmission volume and delay are reduced by completing processing locally.According to Gartner's forecast, by 2025, 75%of the data will be generated on the edge side of the data center and clouds.
From 2020 to the present, with the continuous development and popularization of artificial intelligence technology, marginal AI as AI technologyThe new model combined with edge calculations has gradually emerged.edgeAI allows AI computing and decision -making near the location of the actual creation of data, thereby improving real -time, reducing delay, and enhancing privacy protection.
EdgeWith the popularization of 5G and the Internet of Things, the edge AI will be widely used in the fields of smart homes, smart transportation, and intelligent manufacturing.In smart homes, the edge AI can realize the intelligent linkage and personalized services of the device; in intelligent transportation, the real -time scheduling and road conditions of the vehicle can be achieved; in intelligent manufacturing, the automation and intelligence of the production line can be achieved.
You can see, edgeAI has shown huge potential in multiple industries.With the continuous expansion of AI applications, the overall computing power demand will grow.Especially in the fields of autonomous driving, intelligent manufacturing, and smart home, the personalized demand for real -time and safety has made the growth of marginal computing power an important trend.
According toA research prediction conducted by Astuteanalytica, the size of the edge AI market will increase from 1.4 million in 2021 to 8 million in 2027, with a compound interest rate of 29.8%.This growth mainly comes from the strong needs of the Internet of Things, wearable consumption equipment, and the thirst for 5G network coverage for faster calculation.
In this context, hardware technology is constantly innovating and upgrading.AMD Qualcomm and Apple have successively launched flagship chip products with high computing power performance.These products not only have powerful computing capabilities, but also optimize specific application scenarios, thereby improving the cost -effectiveness and efficiency of computing power.
The chip upgrade not only brings the optimization of the product structure and function of the whole machine, but also promotes the formation of a new round of hardware upgrade trends.This upgrade trend will further accelerate the edgeThe popularization and application of AI technology.
In addition to hardware technology, algorithms also need to be continuously optimized. For the characteristics of limited edge equipment resources, researchers are constantly optimizing artificial intelligence algorithms so that they can operate efficiently on the edge equipment.
Specifically, first of all, as the edge calculation of the environment is popular, rightThe real -time and efficiency requirements of the AI algorithm on the edge equipment are getting higher and higher.The algorithm needs to be able to run quickly in an environment with limited resources and make decisions in a few milliseconds to meet the needs of real -time interaction and processing.
Second, in order to run on the marginal equipment with limited resources,The AI model needs to be designed as lightweight, that is, there are fewer parameters and lower calculation complexity.This design allows the model to run efficiently on the edge equipment while maintaining better performance.
Then, model compression and acceleration technology become optimized edgeImportant means of AI algorithm.Through models such as pruning, quantification, knowledge distillation, etc., the number of parameters and calculation of the model can be reduced, the speed of the model can be improved, and the accuracy of the model can be maintained.
In addition,AI technology is used to optimize the performance and efficiency of marginal computing, including data processing, energy consumption optimization, model training and other aspects.At the same time, edge computing also optimizes the performance and efficiency of the AI model, so that the model can better adapt to the environment and needs of marginal equipment.
Data pre -processing isThe important steps of AI algorithm training are essential for improving model performance.In the edge AI, because data often needs to be processed locally, data pre -processing technology also needs to be optimized to improve the quality and model performance of the data.
EdgeThe optimization of the AI algorithm involves not only the algorithm itself, but also the collaborative optimization of hardware and software.For example, by optimizing the algorithm to adapt to the characteristics of specific hardware, or to optimize the hardware to better support the operation of the algorithm, the overall performance of the edge AI system can be improved.
In addition, in terms of edge computing platforms, major technology companies and cloud computing service providers have also launched artificial intelligence computing platforms for marginal devices to provide convenient marginsAI development tools and resources.These platforms support the full -process service from model training to deployment, which reduces the development threshold of the edge AI application.
All in all, with the continuous progress of technology, the edgeAI has gradually been applied in many fields. At the same time, marginal AI hardware technology and algorithms, edge computing platforms, etc. are still continuously innovating and optimizing.With the continuous development of marginal AI technology, more innovative hardware products and application scenarios will appear in the future.These innovations will promote the further popularization and application of marginal AI technology.
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