Edge AI hardware technology, algorithms, and platforms are constantly innovating/upgrading and optimizing

The Development History of Edge AI

Edge AI can be widely applied in various industries and scenarios, including manufacturing, healthcare, energy, retail, transportation, and more. For example, in intelligent manufacturing, edge AI can process data on the production line in real time, achieving rapid decision-making and optimization; In intelligent transportation, edge AI can process traffic signals and sensor data to achieve intelligent traffic control and safety monitoring.
How did edge AI develop? This starts with the earliest cloud computing. In the early 2000s to 2005, the rise of cloud computing, as a form of distributed computing, decomposed massive data processing programs into countless small programs, and processed and analyzed them through a system composed of multiple servers. At this stage, cloud computing solves the problem of task distribution and merging calculation results, providing the possibility for massive data processing.
After the 2010s, the Internet of Things (IoT) and 4G/5G wireless networks became popular. With the rapid increase of IoT devices, the amount of data generated by network edge devices rapidly expanded, reaching the Zebyte (ZB) level. The popularity of 4G and 5G wireless networks has further promoted the data transmission capabilities of edge devices, but it has also posed challenges to data processing capabilities, network transmission bandwidth load, and personal privacy protection.
By the middle and late of 2010, facing the limitations of cloud computing in edge data processing, edge computing was proposed as a new computing model. Edge computing emphasizes capturing and processing data near data sources or terminals, and reducing data transmission volume and delay by completing processing locally. According to Gartner's prediction, by 2025, 75% of data will be generated on the edge side outside the data center and cloud.
From 2020 to now, with the continuous development and popularization of AI technology, edge AI, as a new model combining AI technology with edge computing, has gradually emerged. Edge AI allows for AI computation and decision-making near the actual location of data creation, thereby improving real-time performance, reducing latency, and enhancing privacy protection.

Edge AI hardware technology and algorithm optimization trends

With the popularization of technologies such as 5G and the Internet of Things, edge AI will be widely applied in fields such as smart homes, intelligent transportation, and intelligent manufacturing. In smart homes, edge AI can achieve intelligent linkage and personalized services of devices; In intelligent transportation, real-time vehicle scheduling and road condition prediction can be achieved; In intelligent manufacturing, automation and intelligence of production lines can be achieved.
It can be seen that edge AI has shown enormous potential in multiple industries. As the scope of AI applications continues to expand, the overall demand for computing power will continue to grow. Especially in fields such as autonomous driving, intelligent manufacturing, and smart homes, personalized demands for real-time and safety are highlighted, making edge computing power growth an important trend.
According to a study conducted by Astute Analytica, the size of the edge AI market is expected to increase from 1.4 million in 2021 to 8 million in 2027, with a compound annual interest rate of up to 29.8%. This growth is mainly due to the strong demand for the Internet of Things and wearable consumer devices, as well as the thirst for faster computing speed in 5G network coverage.
In this context, hardware technology is constantly innovating and upgrading, and mainstream chip manufacturers such as Intel, AMD, Qualcomm, Apple, etc. have successively launched flagship chip products with high computing power performance at the edge. These products not only have powerful computing power, but are also optimized for specific application scenarios, thereby improving the cost-effectiveness and efficiency of computing power.
Chip upgrades not only bring optimization of the overall product structure and functions, but also drive the formation of a new round of hardware upgrade trends. This upgrading trend will further accelerate the popularization and application of edge AI technology.
In addition to hardware technology, algorithms also need to be continuously optimized. In response to the limited resources of edge devices, researchers are continuously optimizing artificial intelligence algorithms to enable them to run efficiently on edge devices.
Specifically, first of all, with the popularization of edge computing environment, the real-time and efficiency requirements of AI algorithm on edge devices are becoming higher and higher. Algorithms need to be able to run quickly in resource constrained environments and make decisions within milliseconds to meet real-time interaction and processing needs.
Secondly, in order to run on edge devices with limited resources, AI models need to be designed to be lightweight, with fewer parameters and lower computational complexity. This design enables the model to run efficiently on edge devices while maintaining good performance.
Furthermore, model compression and acceleration techniques have become important means of optimizing edge AI algorithms. By using techniques such as model pruning, quantization, and knowledge distillation, the parameter and computational complexity of the model can be reduced, and the inference speed of the model can be improved while maintaining its accuracy.
In addition, AI technology is used to optimize the performance and efficiency of edge computing, including data processing, energy consumption optimization, model training, etc. 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 edge devices.
Data preprocessing is an important step in AI algorithm training and is crucial for improving model performance. In edge AI, as data often needs to be processed locally, data preprocessing techniques also need to be optimized to improve data quality and model performance.
The optimization of edge AI algorithms not only involves the algorithm itself, but also needs to consider the collaborative optimization of hardware and software. For example, optimizing algorithms to adapt to specific hardware characteristics, or optimizing hardware to better support algorithm operation, can improve the overall performance of edge AI systems.
In addition, in terms of edge computing platforms, major technology companies and cloud computing service providers have also launched artificial intelligence computing platforms for edge devices to provide convenient edge AI development tools and resources. These platforms support a full process service from model training to deployment, reducing the development threshold for edge AI applications.

Write at the end

In a word, with the continuous progress of technology, edge AI is gradually applied in many fields. At the same time, edge AI hardware technology and algorithms, edge computing platform, etc. are still innovating and optimizing. With the continuous development of edge AI technology, more innovative hardware products and application scenarios will emerge in the future. These innovations will further promote the popularization and application of edge AI technology.