What is Edge AI- The New Wave of AI?

What is edge AI

What is Edge AI?

Edge AI refers to the deployment of AI applications in physical devices. The term “edge AI” refers to the fact that the AI computation is done near the user at the network’s edge, close to where the data is located, rather than centrally at a cloud computing facility or private data center.

But Why Now?

Every industry is looking to expand automation to improve operations, efficiency, and safety.

  1. Maturity of neural networks: Neural networks and related AI infrastructure have finally progressed to the point where generalized machine learning is possible. Organizations are learning how to train AI models successfully and deploy them in production at the edge.
  2. Advances in compute infrastructure: To run AI at the edge, powerful distributed processing capacity is necessary. Recent developments in extremely parallel GPUs have allowed neural networks to be executed.
  3. IoT device adoption: The Internet of Things widespread adoption has fostered the development of big data. We now have the data and devices needed to deploy AI models at the edge thanks to the sudden ability to collect data in every element of a business — from industrial sensors, smart cameras, robots, and more.

When we integrate edge computing with artificial intelligence, we obtain an unbeatable combination.

So, How does Edge AI can help you generate more business?

Edge AI improves decision-making, secures data processing, enhances user experience through hyper-personalization, and reduces costs by speeding up operations and making devices more energy efficient.

Real-time Analysis and Automated Response

Edge technology responds to consumer requirements in real-time because it analyses data locally rather than in a faraway cloud delayed by long-distance connectivity. The most prominent Edge AI examples include how software can handle data and machine learning using deep learning algorithms in autonomous Edge AI applications such as autonomous vehicles.

Information Security and Privacy

AI can study real-world data without ever exposing it to a human, considerably boosting privacy for anyone whose look, voice, medical image, or other personal information must be analyzed. Edge AI improves privacy even further by storing data locally and transferring only the analysis and insights to the cloud. Even if some data is submitted for training purposes, it can be anonymized to safeguard the identities of the users. Edge AI reduces the difficulties associated with data regulatory compliance while safeguarding privacy.

High availability

Because data processing does not require internet access, decentralization and offline capabilities make edge AI more robust. As a result, mission-critical, production-grade AI applications have increased availability and reliability.

Reduced Cost

Edge can deliver considerable cost savings to your firm due to the scalability of analytics and reduced latency in making crucial choices. In addition to saving time, the edge can conserve bandwidth by reducing the need for data transfer. This also improves the energy efficiency of the equipment.


According to IDC, by 2025, there will be 41.6 billion linked IoT devices producing 79.4 zettabytes of data. As data volumes increase, new novel methods for effective analysis and data processing are required. Large volumes of data are frequently involved in edge AI use cases. When processing video image data from hundreds or thousands of various sources at the same time, sending the data to a cloud service is not an option.

How Does Edge AI do its Magic?

To sense, recognize objects, understand speech, talk, walk, operate automobiles and perform other human-like tasks, machines must effectively imitate human intelligence.

Let’s Look at Some Use Cases of Edge AI

Edge AI technology arose from the convergence of artificial intelligence, machine learning, and edge computing. This combo was designed to bring deep learning artificial intelligence systems closer to the surface. Edge AI applications are being used in a variety of industries and use cases.

There is always a lot of hype around new technology, but there are several actual reasons behind the Edge AI market’s rise.

Let’s see what this new technology holds for us in the future.

Edge AI: Trends and the Future

Edge AI is clearly growing in popularity. But this is merely the beginning. There are several trends that have emerged in the domain. Take a look at it.

Increase in edge data centers

More than five million servers will be placed at the edge by 2024. These data centers would only increase in number due to a variety of variables such as:

  1. 5G network proliferation,
  2. IoT proliferation,
  3. SDN, and NFV technology
  4. AR and VR video streaming

Customer experience

People want services to be smooth and seamless. Data transport delays will be eliminated via edge AI. Furthermore, as sensors, cameras, GPU processors, and other hardware become more affordable, both bespoke and highly productized Edge AI solutions become more widely available.

Edge AI and IIoT convergence

When it comes to AI adoption, manufacturing industries, particularly those that have included IoT, stand out as the most prominent. In the coming years, we will see the convergence of IIoT and Edge AI in use cases centred on sensors and cameras for inspection, preventative, and predictive maintenance.

AI Edge Applications

Edge AI application development enables powering of scalable, mission-critical, and private AI systems. Because Edge AI is still a relatively young technology, many additional applications are envisaged in the near future.

  1. Smart AI: Vision encompasses computer vision applications such as live video analytics that are used to fuel AI vision systems in a variety of sectors.
  2. Smart energy: Connected wind farms are one example of a smart energy application. A study compared the data administration and processing costs of a remote wind farm utilising a cloud-only system vs a mixed edge-cloud solution. The edge-cloud solution was found to be 36% less expensive than the cloud-only system.
  3. Smart Healthcare AI application: Healthcare applications like as remote surgery and diagnostics, as well as patient vital sign monitoring, are largely dependent on edge devices that perform AI at the edge.
  4. Smart Factory: Applications such as smart machines, are designed to improve safety and production.

Get Started with Edge AI Technology Today

If you’ve made it this far, you’re undoubtedly wondering how Edge AI solutions could help your company.



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Terasol Technologies

Terasol Technologies

An app development agency taking small steps towards building a brighter future. Visit us at http://www.terasoltechnologies.com/