Edge computing when combined with artificial intelligence produces a combination known as edge AI which is a formidable combination. It refers to the implementation of AI within the edge devices that make it possible to analyze data and produce decisions in real-time irrespective of cloud status.
Citing the need for instantaneous decision-making as well as minimum delays in data processing, edge AI is hence becoming the new frontier for organizations in different industries.
At its simple level, edge AI means leveraging of machine learning models and AI algorithms at the edge node on various types of connected devices like smartphones, IoT sensors, robots, and autonomous vehicles. Edge AI works with data locally instead of sending to more distinct cloud servers; therefore, it significantly cuts the latency and bandwidth usage and improves privacy and security levels.
This approach is mostly helpful when the timely response to the changing environment is of crucial importance in areas such as autonomous car driving, industrial processes, and health care systems.
Application of edge AI hardware is growing rapidly and the market is expected to grow to $38B. Some projections AMD and disability cost to be as high as 87 billion by 2030 as compared to $6. 88 billion in 2021. This growth is as a result of rising demand for low-latency processing in AI applications and the use of numerous edge devices that are AI-enabled.
Silicon giants, including NVIDIA, Intel, and Qualcomm are already focusing on the design of proprietary edge AI silicon that will enable the execution of sophisticated models on restricted edge hardware.
Another strength that can be associated with edge AI also lies in their work in situations with low internet connections or no connection at all. This makes it especially useful in areas that are hard to reach or dangerous such as oil rig, mines and disaster scenes where cloud based AI solutions cannot be implemented easily. Likewise, the processing of sensitive data at the edge obviates privacy issues and assists organizations in meeting data protection legislation.
Healthcare is among the initial industries that integrated edge AI to monitor patients’ progress, analyze medical images, or offer treatment plans.
For instance, edge AI wearing devices would used to provide round the clock supervision of healthrelated parameters and timely alert health professionals of developing complications. In industry, edge AI can be used in monitoring the health of the equipment to alert the users of oncoming failure before it happens, thus reducing downtime.
5G networks are continuously being adopted across the world, and thus the integration of 5G and edge AI is believed to bring more opportunities. Real-time performance offered by 5G network will eventually be implemented for the advancement of edge AI solutions prominently in the aspects of augmented reality, virtual reality, and autonomous systems.
This pairing is expected to fast track the growth of smart cities where edge AI sensors and other gadgets could facilitate timely control of the traffic flow, energy usage, among others.
Nevertheless, there are some issues that have to be faced with the development of edge AI. The first of them is to come up with improved AI models that are capable to work on the limited computational capacity and power of the targeted devices.
Some of the steps being proposed to solve this problem include model compression and quantization which is under development by various researchers. Furthermore, other areas that continue to attract significant concerns are security and reliability of the AI models that are locally installed in edge devices.
There are more possibilities for the development of the edge AI based on the integration with other nowadays trending technologies such as blockchain and quantum computing. In the future, edge AI will bring the change on how we are interacting with the devices present in our lives as it makes the smart devices even smarter and capable of comprehending more our needs and demands.
This is a rather fresh concept in the field of AI, edge AI brings the power of AI into everyday devices we use. In future after overcoming present limitations, it holds the possibility to act as a catalyst in encouraging innovations in a variety of fields and redefining man-machine interfaces.
Expectations for the development of edge AI in the following years will be the significant surge of this concept on the edge changing the technological foundation and creating new opportunities for computation.
Leave a Reply