TOP TAKES is IoT Sources’ filtered content channel, bringing you the most important breaking news and notable events surrounding the Internet of Things. Today’s post originated from: www.eweek.com and written by Chris J. Preimesberger.
Intel this week introduced what it calls the OpenVINO (Open Visual Inference & Neural Network Optimization) toolkit. This toolset is designed to fast-track development of high-performance computer vision and deep learning inference applications at the edge.
Intel plays a lot of roles in the IT business besides making processors with microscopic transistors for servers, PCs, the internet of things, and mobile devices. It also makes security hardware and software, memory and programmable enterprise solutions, 5G connectivity hardware and software and a list of others too long to note here.
But one of the greenest fields coming into the venerable chipmaker’s view here in mid-2018 has to do with what’s called “the edge”—that mysterious, nebulous and more distributed area outside the data center where a lot of computing is starting to happen and will be happening more and more as time goes on.
We’re hearing a lot about this lately, largely because our devices (smartphones, laptops, tablets, IoT devices) on the fringes of centralized systems can hold much more information and do more with it than in years past. Intel wants to make more and more of the infrastructure for these devices and systems.
What is Edge Computing?
Definition: Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data. This reduces the communications bandwidth needed between sensors and the central data center by performing analytics and knowledge generation at or near the source of the data. This approach requires using resources that may not be continuously connected to a network, such as laptops, smartphones, tablets and sensors.
By its very nature, edge computing—which also includes these devices communicating with each other via Bluetooth and other non-cloud methods—decreases workloads that used to be processed inside 24/7 cloud computing systems. This not only increases the efficiency of computing and data applications but also promotes further implementation of emerging technologies, such as artificial intelligence and 5G bandwidth.
Intel is determined to become a leader in providing the smarts for edge computing. We know this because Tom Lantzsch, Intel senior vice president and general manager of the Internet of Things (IoT) Group, explained it in a blogpost.
“We have been working hard [for the last year] to define and develop a data-driven technology foundation for industry innovation,” Lantzsch said. “Our strategy is to drive end-to-end distributed computing in every vertical by focusing on silicon platforms and workload consolidation at the edge.”
Enterprise Video: Low-Hanging IoT Analytics Fruit
A big part of this strategy in the early years of edge computing involves the low-hanging fruit of enterprise video. These are the IoT use cases that are being transformed first, because old-time analog video is very expensive to use, maintain and store, whereas digital video handled inside a cloud or edge-computing system is much more effective, easier to use and maintain, and easier and cheaper to store.
“We are seeing significant growth in IoT markets worldwide, driven in part by a dramatic increase in vision applications, particularly those leveraging artificial intelligence (AI),” Lantzsch said. “These imaging and video use cases span nearly every IoT segment. They include finding product defects on assembly lines, managing inventory in retail, identifying equipment maintenance needs in remote locations and enabling public safety in cities and airports. They all leverage high-resolution cameras and create extraordinary amounts of data, which needs to be aggregated and analyzed.”
To address this expansive data growth, Intel this week introduced what it calls the OpenVINO (Open Visual Inference & Neural Network Optimization) toolkit. This development toolkit is designed to fast-track development of high-performance computer vision and deep learning inference applications at the edge.
OpenVINO Integratable into Other Apps
Intel customers can integrate the OpenVINO toolkit with devices running AWS GreenGrass for performing machine learning inference at the edge, for one example.
Processing high-quality video requires the ability to rapidly analyze vast streams of data near the edge and respond in real time, moving only relevant insights to the cloud asynchronously, Lantzsch said. To process video data efficiently, enterprises need the right solution for the job. Unlike others with a one-size-fits-all philosophy, Intel believes the market requires a portfolio of scalable hardware and software solutions to move into an intelligent data-powered future.
This includes widely deployed and available Intel computing products, including those with integrated graphics, Intel FGPAs (field-programmable gate arrays) and Intel Movidius VPU (vision processing unit).
Thus, OpenVINO is the latest offering in the Intel Vision Products lineup of hardware and software that is dedicated to transforming vision data into business and security insights. This type of video system can identify faces, patterns of traffic, specific vehicles through license plates and so on, so that city, security and other types of administrators can glean knowledge—and find bad guys—in video surveillance.
Why Move Video Analytics to the Edge?
Adam Burns, Intel’s Director of Computer Vision and Digital Surveillance, told eWEEK said there are two main reasons for video and accompanying analytics to move to the edge.
“The first is economic, because the data itself lends itself to processing at the edge. The second is application-dependent, meaning either that the data is such that you want to maintain security and privacy, or you want to take action immediately on it, and keep it resident to where it’s happening,” Burns said.
There’s no one solution that can map to all the video analytics capabilities that are now being used, so Intel is offering an opportunity to set a foundational brick with this toolset.
The OpenVINO toolkit provides a high-performance solution for edge-to-cloud video analytics and deep learning. It empowers developers to deploy deep-learning inference and computer vision solutions, using a wide range of common software frameworks such as TensorFlow, MXNet and Caffe.
Intel’s vision products and the OpenVINO toolkit are being used by global partners such as Dahua for smart city and traffic solutions; GE Healthcare in medical imaging; and Hikvision for industrial and manufacturing safety. Additional users currently include Agent Vi, Current by GE, Dell and Honeywell.
For more information on the toolkit, go here.
Chris J. Preimesberger is Editor of Features & Analysis at eWEEK, responsible in large part for the publication’s coverage areas. In his 12 years and more than 3,900 stories at eWEEK, he…