How AI Edge Computing Is Changing Artificial Intelligence

Artificial intelligence is developing rapidly, and we can expect to see significant leaps forward in the coming years. While predicting the exact nature of these leaps forward is difficult, we can identify one of the key drivers of change — the growth of AI edge computing technology.

In this article, we're going to be looking at AI edge computing in more detail and discovering how it is helping artificial intelligence to meet the needs of an evolving business landscape.

AI edge computing puts data processing at, or close to, the source of data. It depends on powerful high performance computing components to handle this data onsite — components like the NUC-TGY Fanless NUC Slim System or the ACS10-TGU Fanless Compact System.

As you might be able to tell from the names of these solutions, they are physically small, which means they can be deployed right at the heart of supply chains, manufacturing lines, and other industrial operations. Both solutions feature Intel's Tiger Lake processors, designed to deliver high computing performance from compact mobile devices.

Other AI edge computing components include GPU expansion systems like the HPS-ERSD4A for ultra-high speed computing requirements and the NVIDIA-based AIB-NINX-SC. These components increase the versatility and effectiveness of edge computing systems when they are deployed.

Edge technology is something of a game changer for AI computing. It essentially disrupts the traditional channels of data gathering, processing, and deployment, completely changing the structure of AI systems.

But how will this impact AI computing and the businesses and organizations that rely on it?

One of the key ways in which AI edge computing is changing the industry is by making AI more accessible. Large numbers of businesses are already using AI as part of their high performance computing setup, but this has traditionally been expensive.

Businesses need an enormous amount of network bandwidth to support the constant flow of data. This data flows from solutions to a data center, often hosted in cloud architecture. Once processed, the data must flow back to the facility, ready to be acted upon.

Edge computing removes several steps from this convoluted process. The data is processed close to the source, so businesses don't need to constantly expand their bandwidth. They also don't need to pay for the additional cloud resources they had previously been using to leverage AI. Combined, this makes using AI far more cost-effective and far more accessible.

Organizations with limited budgets or in areas of low connectivity can still benefit from the AI revolution.

It's true to say that artificial intelligence has a sustainability problem. Large amounts of power and resources are required to maintain data centers, and the constant flow of information has a significant impact on the environment. While society is already reaping the benefits of AI, this sustainability issue still needs to be solved.

This is another area in which AI edge computing is changing the broader landscape. Processing information at or close to the source decreases the need for large data centers made up of energy-hungry high performance computing components. It also eliminates much of the data flow, which, in turn, reduces the carbon footprint of operation.

All of this represents only a partial solution. AI still has some distance left to go before it can be considered truly sustainable. But AI edge computing is at least a step in the right direction.

AI depends on data, making data a highly valuable commodity in the modern world. Much of this data is also sensitive, which means organizations must take steps to protect their storage and processing systems without compromising on the benefits of AI.

With edge computing, the developers of AI systems are able to eliminate one of the key vulnerabilities in the industry — the transportation of data. While servers and storage systems can be, and are, targets for cybercriminals, these are relatively easy to defend. When data is in transit, however, it becomes vulnerable.

The very nature of edge computing reduces the need for data transit. As mentioned above, processing takes place close to the source, so there are far fewer opportunities for criminal operators to intercept and steal data.

Last but certainly not least on our list, edge computing is helping AI become more capable and more powerful. If systems rely upon long-distance data transfer and remote processing, operations experience significant latency. With edge computing, this latency is greatly reduced.

With onsite data processing, data takes a negligible amount of time to travel from the source to the edge processing unit. The insight is derived from this data in a fraction of the time, and AI-based decisions are taken almost instantly. Businesses need their systems to be decisive, accurate, and agile, and edge technology can make all three of these aims a reality.

Data can still be stored onsite if required, and it can also be transferred and used elsewhere. But in terms of onsite operations, the immediate impact of edge computing is going to be enormous and will help businesses gain a real competitive advantage over their rivals in the market.

Here at Avalue, we are proud to offer an industry-leading range of components designed to support the next generation of digital technology. This includes AI edge computing technology, helping more businesses leverage the power of AI and shaping its development.

Take a look at our product range today and find what you need, or reach out to our team directly if you have questions or queries about our products.