Edge AI core technology and industrial chain

Edge AI is the artificial intelligence technology that runs on edge devices. Edge devices include smartphones, smart speakers, smart home devices, cameras, etc. These devices usually have a certain amount of computing power and storage capacity, and can carry out a certain degree of data processing and analysis. The core technologies of edge AI mainly include machine learning, deep learning, computer vision, natural language processing, and so on.

The application scenarios of edge AI are very broad. For example, voice assistants on smartphones can realize speech recognition and semantic understanding through edge AI technology, providing users with intelligent voice interaction functions. Smart home devices can realize functions such as face recognition and behavior analysis through edge AI technology to provide a safer and smarter home experience. Smart cameras can achieve real-time target detection and tracking through edge AI technology, which is used in fields such as security monitoring and intelligent transportation. Edge AI can also be applied to drones, intelligent vehicles, industrial automation and other fields to achieve more efficient and intelligent systems.

The continuous progress of edge AI technology is mainly reflected in the following aspects:

1. Model compression and optimization: Due to the limited computing and storage resources of edge devices, edge AI needs to compress and optimize large-scale deep learning models to adapt to the hardware conditions of edge devices. Model compression techniques include pruning, quantization, low-rank decomposition, etc., which can reduce the volume and calculation amount of the model and improve the operation efficiency of the model.

2. Incremental learning and transfer learning: Edge devices have limited resources and cannot store and process large amounts of data. Through incremental learning and transfer learning, online learning and transfer learning of models can be carried out on edge devices, reducing the dependence on large-scale training data and computing resources, and improving the intelligence level of edge devices.

3. Distributed computing and collaborative learning: Edge devices are usually in a distributed environment, and the computing and model training tasks on edge devices can be distributed to multiple devices for parallel computation through distributed computing and collaborative learning technologies, so as to improve computing efficiency and model accuracy.

The industrial chain of edge AI mainly includes chip manufacturers, equipment manufacturers, algorithm and software developers, and cloud service providers. Chip manufacturers are primarily responsible for designing and producing high-performance, low-power AI chips for edge devices. Device manufacturers are responsible for integrating chips into various edge devices, providing products with edge AI capabilities. Algorithm and software developers are responsible for developing the core technologies and algorithm models of edge AI to provide AI applications on edge devices. Cloud service providers provide cloud computing and storage resources to support model training and model update of edge devices.

With the continuous development of edge AI technology and the continuous expansion of application, the development of edge AI industry chain also shows the following trends:

1, chip specialization and customization: In order to meet the needs of different application scenarios for computing and storage capabilities, chip manufacturers will introduce more specialized and customized artificial intelligence chips to provide higher performance and lower power consumption solutions. For example, dedicated neural network accelerators on edge devices can improve the speed and energy efficiency of models.

2, the intelligence and integration of edge equipment: edge equipment will be more and more intelligent, with more perception and computing power. It is not only capable of simple data processing and analysis, but also complex decision making and reasoning. Edge devices will also be more integrated, not only providing edge AI capabilities, but also working with other smart devices and cloud services.

3, cloud-edge collaboration and edge-cloud collaboration: The collaboration between cloud and edge devices will become closer. The cloud can provide more powerful computing and storage resources for model training and model updating on edge devices. Edge devices can provide faster response and lower latency for real-time data processing and decision making. Cloud-edge collaboration and edge-cloud collaboration will become an important direction of edge AI development.

In short, the continuous progress of edge AI core technologies and the continuous expansion of applications will promote the development and innovation of edge AI industrial chain, and bring smarter and more convenient experiences to people's lives and work.