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11 Impressive Benefits and Use Cases of Edge AI

Edge AI

11 Impressive Benefits and Use Cases of Edge AI

Shandra Earney / May 11, 2022

The ability to process AI on devices or close to the data source unlocks impressive benefits such as superior reliability, low latency, improved privacy, the incredibly efficient use of bandwidth, and more. 

Moving AI to the network’s edge creates new opportunities for AI applications with new services and products. 

Let’s unpack the 11 impressive benefits of Edge AI.

1. Edge AI improves bandwidth efficiency

When AI algorithms can process data close to the Edge, the need to transmit information over the network is reduced. This allows bandwidth to be used more efficiently, which results in impressive high-performance outcomes (even on constrained networks) and reduced operating costs.

2. Latency is reduced with Edge AI

With fully on-device processing, users can enjoy swift response times. There’s no wait time due to information needing to return from a remote server. In addition, hybrid configurations (e.g., combining the Edge with nearby side-server processing) reduces the time required for communications with cloud-based services. 

Reduced latency improves customer experience and becomes particularly important in critical AI use cases like security. For example, with Face Recognition technology, speed matters as fast response times are crucial regardless of how a business uses Face Recognition.

For instance, not identifying banned individuals quickly enough could cause tremendous harm to a company. Use cases such as this make Edge Face Recognition a fantastic security option for businesses.

3. Edge AI decreases size and weight

Conventional processing technologies consume a significant amount of power and are often costly. On the other hand, Edge AI chips can deliver incredibly accurate results while using substantially less power. Because Edge AI chips typically use between 1 to 5 watts (and sometimes even less), they reduce the need for cooling fans and heat sinks. 

A finished product that utilizes Edge AI technology is smaller in size and weight, resulting in simplified, smaller designs. This provides designers with a broad range of innovative design options and allows devices to suit architectural limitations specific to their individual use cases.

4. Enhanced privacy with Edge AI

Edge AI enables enhanced privacy protection. Processing data locally offers much better security for users’ personally identifiable information than sending it across networks where it can be vulnerable to cyberattacks. 

Additionally, managing data at the source allows users to comply with policies surrounding how far data is permitted to travel and where it’s stored.

5. Using Edge AI lowers hardware costs

With Edge AI, users no longer need to rely solely on cloud services for data storage and processing. This offers the potential for a lower total cost of ownership. 

Promising data is underpinning this claim. For example, a study from Deloitte highlights that an Edge AI chip will cost about as much as a smartphone’s processor while offering lower power consumption and better performance than conventional processor architectures. 

In a similar vein, a study from HPE suggests that the total cost of ownership from using the cloud for data analytics is 1.7 to 3.4 times higher than comparable on-premises (Edge) deployments.

6. Function offline with Edge AI

Due to improvements in chip design, Edge AI devices offer advanced functionality without requiring a network connection. This means that devices can continue to process data and perform their tasks in the face of network outages and intermittent connectivity.

7. Edge AI affords high availability

Edge AI’s offline capabilities and decentralization make it more robust because it can offer quick services during cyberattacks or network failures. 

Deploying AI tasks at the Edge promises substantially higher availability and robustness. 

The high availability of Edge AI also aids equity, diversity, and inclusion. Edge AI is inherently democratizing, as it gives control to the people trying to solve problems in the field. Additionally, not needing to depend on expensive cloud services or the availability of communication networks broadens AI’s access to populations across the world while empowering a more extensive pool of innovators.

8. Improved model accuracy with Edge AI

AI models grow increasingly accurate as they train on more data. With Edge AI, the application of training models and inference happen directly on Edge devices. 

When an Edge AI application finds data that it can’t confidently or accurately process, the AI can learn from it at the Edge. Therefore, the longer an AI model is in production at the Edge, the more accurate the model will become.

9.  Edge AI means sustainable AI

Conventional AI applications require a significant amount of computing power, and as AI continues to increase, it will consume more energy and produce more carbon emissions. 

Even today, AI produces more carbon than the airline industry. 

Deploying AI at the Edge enables local, low-power data processing. This reduces greenhouse gas emissions and energy consumption, making Edge AI a sustainable AI option.

10. Edge AI cuts costs compared to cloud-based AI

AI demands a significant amount of computing power. With cloud computing, this can get expensive, and therefore, Edge AI has a cost advantage over cloud-based AI solutions. 

The reduced bandwidth, decreased power consumption, and low chip prices of Edge AI also contribute to this cost advantage. 

11. Edge AI offers increased levels of automation

Machines and assets deployed at the Edge can be more readily trained to perform autonomous tasks.

Use cases that benefit tremendously from Edge AI

The benefits of Edge AI we discussed above directly contribute to how the technology continues to make its way into our everyday lives.  

In fact, several AI use cases are best suited for the Edge, where processing happens at or close to the data source, reducing latency, lowering costs, increasing data privacy, and improving reliability. 

The following use cases showcase some of the most significant advantages that Edge AI can offer to our everyday lives. These use cases are:

1. Secure banking

Edge AI can auto-detect transactions triggered through different payment platforms in other countries or locally. It can also detect abnormal spending and other unusual activities. Edge AI will immediately send notifications to users asking them to confirm these transactions, offering users a great deal of financial protection.

2. Smart content and ad services

Edge AI enables advertising companies to deliver tailored news and information to users. These smart services show customers new products and services that will interest them.

3. Edge AI delivery logistics systems

Edge AI can provide speedy tracking information to ensure that packages arrive on time and at the right place. It also ensures that packages can be accurately tracked throughout their journeys.

4. Home security systems

In addition to seeing packages that arrive at a property, Edge AI can also see people coming to a user’s property, sending an instant alert if there is any unusual activity.

5.  Edge AI powered weather tech

Edge AI weather technology accurately tells users what the weather will be like on a particular day.

6. Recommendation engines

Edge AI backed recommendations can provide users with content or product recommendations that they’ll want to see.

7. Traffic control systems

Coordinated signal lights for traffic-flow efficiency can help users navigate alternate routes when traffic piles up. It can also predict how long it will take someone to get to where they need to be.

8. Airport security Edge AI

Airport security is starting to get faster and smarter with Face Recognition. This was tested recently in Hartsfield-Jackson Atlanta International, the world’s busiest airport. The study found that travelers could move through security much faster thanks to the streamlined security afforded by Face Recognition technologies.

9. Edge AI enhanced entertainment

Smart-features like in-context player information enrich and bring users closer to entertainment experiences – from football to movies.

10. Automatically generated communications

With Edge AI, you can trigger birthday and holiday greetings automatically. You can also create photo and video albums to stay connected with loved ones.

11. Expert manufacturing and quality control

Manufacturing and quality control highlight a promising Edge AI use case. 

Advanced machine vision (video analytics), an example of industrial Edge AI, can monitor product quality around the clock reliably and with great precision.

Video analytics can detect the tiniest quality deviations that are virtually impossible to notice with the human eye.

Edge AI technologies make it all possible

Edge AI technologies help make everything discussed above possible. As we continue to build ever-stronger technologies, it’s important to think about how they aid our quality of life. 

From enabling algorithmic efficiency to granting access to users and innovators in diverse locations, it’s clear that Edge AI and all of its impressive benefits are positively impacting the world today.

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About the Author

    Shandra is a Writing and Media and Digital Cultures graduate. She is a twice published author in an independent literature magazine. 

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