Edge Computing: Enabling Real-Time Insights and Actions

Gary Brown, AI product marketing director at Intel, said the ability to conserve overall power may improve through the use of edge computing, as transferring data to cloud servers or edge cloud servers can require more power consumption than keeping it on devices. “Depending on the amount of computing you have to do, you can do it in the device,” he said. “That translates to faster results, lower latency and lower power consumption.”

AI and the Computing Continuum

Edge computing can help companies migrate to a future in which some latency-sensitive IoT applications, such as those involving autonomous vehicles or management of critical infrastructure, more often will require instantaneous decision-making. It’s part of what will help IoT deliver on all the hype it has been saddled with.

“IoT can help us realize the true value of the Internet,” ClearBlade’s Simone said. “What we have done the last 25 years or so has been great [with social media, e-commerce and customer relationship management innovations], but it’s low hanging fruit. What’s going to a be a big deal is the next 25 years is when we see trains, cars, manufacturing systems and critical infrastructure get updated and modernized because of IoT.”

Edge computing is just one piece of the puzzle, however. Artificial intelligence is another, enabling the detection and recognition of patterns or changing circumstances to be processed at the edge. AI silicon could be used in endpoint devices or in edge nodes, such as gateways, to allow AI inference to help devices–either autonomously or through their human operators–use data-driven insights to take the right course of action in managing an application.

One example of how this would work, Brown said, is an automated retail store operation, in which cameras and other sensors track availability of inventory, shopper handling of inventory, and a cashier-less or mobile-enabled checkout process. AI can be used in the edge devices to make sure that an item is added to the customer’s bill when placed in a shopping cart, or deleted if it is removed from the cart. Detection of low inventory can trigger reordering or restocking.

In different kind of example, the edge devices could be connected cameras in a smart city that identify the amount of traffic congestion in an intersection. These devices could be connected to nearby appliances that aggregate cameras and run video analytics across multiple cameras. And those appliances are connected to the network where intelligence can be aggregated and applied to controlling traffic signals to better manage congestion throughout the city.

“You need to have a powerful neural network that can do all of the detection and classification in that environment,” Intel’s Brown said. “The data center has always had a lot of AI, but now you see it at being used at the edge. AI at the edge now may be growing faster than AI at the data center [according to data Brown attributed to Intel, IDC and Gartner].”

That doesn’t mean that the need for cloud computing in enterprise IoT networks will dissipate. Brown and Ressler both referred to “the continuum of computing resources” that allows companies to make adjustments in how they manage their computing needs.