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With 5G, AI at the edge promises a compute-everywhere future

From the factory floor to delivery robots, innovation is moving fast with real-time data processing.

Luxury auto maker Audi is driving full-throttle toward Industry 4.0, using AI inference and computer vision on the factory floor with autonomous robot welders that can react in real time and fix issues that may arise when welding the frame of a car. That’s just one example of how the company is moving toward realizing its ultimate vision of creating smart factories with a scalable and flexible platform that will enable data analytics, communications and processing at the edge, powered by 5G.

In the past, welding required a lot of manual intervention and inspection to ensure sufficient quality, says Nick McKeown, senior vice president and general manager of the network and edge group at Intel, which is working with Audi. Now, with cameras reviewing the quality of the weld the need for human intervention has greatly decreased.

"If you want, or need to process data in real time, you actually have to bring the compute to the data, to the point of data creation and data consumption."
Sandra Rivera

"Edge computing is taking the technology resources we've been developing over many years for the computing industry and using them to analyze and process data at the edge", McKeown says. The concept of edge computing is storing data closer to where it is generated and used—like the factory floor--instead of in the cloud, which means it can be processed in real or near real time.

"If you want, or need to process data in real time, you actually have to bring the compute to the data, to the point of data creation and data consumption", explains Sandra Rivera, executive vice president and general manager of the datacenter and AI group at Intel. Not having to move large amounts of data enhances security, and increases reliability while reducing latency. And because data is kept more private there is an additional layer of data sovereignty available when needed, adds McKeown.

Growing opportunities for 5G at the edge

As telecommunication operators continue rolling out 5G infrastructure, "there are opportunities that start to emerge because the data rate, the latency, the control that you have over the 5G network means that we can start to use it for applications that we would not have previously thought suitable for a cellular technology," McKeown says.

In the Audi factory example, controlling a robot arm in real time requires either a cable, a wire, an ethernet cable that connects to it to guarantee connectivity, the data rate that is needed, and the low latency control—or it has to be replaced with a wireless link, he says.

"Now imagine that robot is moving around. You really don't want a wire trailing around on the floor for other robots to trip over. You'd really like it to be a wireless link", McKeown says. "And the problem is, wi-fi hasn't really gotten there just yet in terms of the quality that you would want. What 5G, in particular private 5G, offers is a much more reliable, much lower latency, much more controlled-by-software experience."

5G relies on the edge

5G promises low latency and high revenue over the next decade as more organizations invest in its potential.
Source: “5G and cloud: How telecom can architect the next cloud era,” Ericsson, February 4, 2021

According to a recent Gartner report, "Predicts 2022: The Distributed Enterprise Drives Computing to the Edge", 5G is the fastest growing segment in the wireless network infrastructure market—and global revenue will likely reach $23.2 billion in 2022. Gartner has further predicted that by 2025, more than 50% of enterprise-generated data will be processed outside a traditional centralized data center or cloud.

Deploying AI applications at the edge with 5G has the potential to generate new revenue sources—and position AI as standard bearer for 5G. Opportunities range across industries including smart manufacturing, smart cities, rich media, enhanced retail logistics, and automated warehouses, among others.

Keeping an eye on obstacles

Because AI is a highly compute-intensive process it is critical to have the right infrastructure optimized for the unique demands of AI workloads at the edge. Another consideration says Rivera is that "Computing takes power. And we know that we have to work within restricted power envelopes when we're deploying on the edge and also computing on small form factor devices, or in areas where you have a hostile environment," Rivera notes.
"Every day, every week, I see a number of different use cases that our customers or their customers have put in place that we would never have thought of."
Nick McKeown

For example, if wireless infrastructure is deployed across the globe, that connectivity will exist in both the coldest and the hottest places on earth, she says. "We design and develop our products on our own, as well as together with customers, for much more power-efficient types of platforms to address that particular set of issues."

There's always more work to do, because there's always more computing people want to do on an ever-limited power budget, Rivera says.

"The other big limitation we see is in legacy applications," she adds. In the case of deploying internet of things (IoT) devices, there is such a broad range of market segments each customer’s environment and individual needs have to be considered.

"Our challenge is, how do we give application developers an easy way to migrate and integrate AI into their legacy applications? When we look at how to do that, first of all, we have to understand that vertical and work closely with customers."

Edge computing takes over

More businesses will take advantage of real-time insights enabled by edge computing.
In 2018,
of enterprise-generated data was created and processed at the edge
By 2025,
of enterprise-generated data is projected to be created and processed at the edge
Source: “What Edge Computing Means for Infrastructure and Operations Leaders,” Gartner, October 3, 2018

The possibilities are endless

The combination of AI and 5G will transform the enterprise and accelerate economic growth, as 5G networks provide the backbone, scalable bandwidth, and remote compute resources to process increasing volumes of data that will fuel the proliferation of AI.

If someone had told McKeown a few years ago there would be smart delivery bots in cities and towns being driven by autonomous vehicles that would walk down sidewalks, climb stairs, and deliver right to someone's door, he would have said that might happen maybe 15 or 20 years from now. Yet, those applications are being tested and rolled out right now.

And that’s just one visible example we will see. That automation and control is happening in warehouses and in factories because of sensors and actuators running on a 5G network. The combination is going to create "a sort of a Cambrian explosion of new ideas" that if we were to try to predict, we would get wrong, McKeown says.

Whatever we think is going to happen by combining new IoT applications with public and private 5G, as well as AI and machine learning at the edge, "will actually shock us", he says. "And that's because it's the wild pioneering west, and it's wonderful, it's exciting, it's terrifying, it's growing, it's expanding. Every day, every week, I see a number of different use cases that our customers or their customers have put in place that we would never have thought of."

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