MWC, Barcelona; last week. Red Hat and Intel have developed a (relatively) easy and cheap edge/cloud computing solution for enterprises to deploy and multiply AI-based applications on private 5G networks. The duo have a reference solution, they said, which easily flexes distributed edge/cloud compute resources to handle escalating AI workloads, from maintenance tasks to production systems, on new private 5G setups in Industry 4.0 venues.
Madrid-based industrial tech consultancy Minsait, part of Spanish information technology and defense systems company Indra, is using the solution with commercial customers, they said, including for wildlife and fire detection on wind farms, drone-based asset management in warehouses, and response management to oil spills. The solution, which combines Red Hat’s OpenShift platform to run private 5G workloads on Intel’s FlexRAN software in its Edge Platform.
The solution can be expanded or customised with third-party apps. “The solution is developer-friendly without sacrificing high-end compute performance for AI applications and workloads,” said Red Hat. They stated: “When deployed, a private 5G network can provide the connectivity, tools, and applications… to infuse AI on the factory floor by running on a modernized, automated, scalable and manageable cloud-native platform”.
They also noted the “enhanced security capabilities” with their joint solution (or just with industrial-grade 5G). In the wind-farm example, Minsait is offering compute flexibility to run a computer vision app to detect protected birds, wildfires, and other “nearby incidents”. The indoor warehouse project is using the same so drones work “collaboratively [and] safely alongside humans” to run inventory management and traceability.
Its private 5G deployments in oil refineries, offshore platforms, and ports are running camera AI apps via the same Red Hat/Intel system for “drones or robots”, and also to “detect oil spills at port entry using intelligent algorithms and data collection from sensors, radar, and infrared cameras”. Writing in a blog post, Red Hat said: “Private 5G is not a static technology; it will need to grow, expand and shift in new directions as business needs evolve.”
It went on: “To address this challenge, Red Hat and Intel have collaborated to create a cloud and edge-native private 5G solution for industrial and cross vertical deployments that is cost-effective and easier to adopt. This enables manufacturers to more readily capitalize on the massive revenue opportunity presented by AI-enabled software-defined operations and factories.”
Meanwhile, Red Hat has been working with NTT, plus NVIDIA and Fujitsu, to “extend the potential” for real-time AI data analysis at the edge, also using its OpenShift platform. The group is working as part of the Innovative Optical and Wireless Network (IOWN) initiative, and have an edge-cloud industrial AI proof (Poc) in Japan, with an edge sensor installation at Yokosuka City connected via an all-photonics network (APN) to a data center in Musashino City.
The APN introduces photonics (optics) based technology to every aspect of the network. The solution also uses “pipeline acceleration technologies” from NTT – in line with IOWN’s combined information-and-comms (ICT) (data-centric; DCI) infrastructure, leveraging the features of its APN. The pipeline tech uses remote direct memory access (RDMA) – between computers, without involving either’s operating system – over the APN.
This way, it efficiently collects and processes large amounts of sensor data at the edge, said Red Hat. “OpenShift provides greater flexibility to operate workloads within the accelerated data pipeline across geographically distributed and remote data centers. NTT and Red Hat have successfully demonstrated that this solution can effectively reduce power consumption while maintaining lower latency for real-time AI analysis at the edge,” it said.
A statement said: “As a result, even when a large number of cameras were accommodated, the latency required to aggregate sensor data for AI analysis was reduced by 60 percent compared to conventional AI inference workloads. Additionally, the IOWN PoC testing demonstrated that the power consumption required for AI analysis for each camera at the edge could be reduced 40 percent from conventional technology.
“This real-time AI analysis platform allows the GPU to be scaled up to accommodate a larger number of cameras without the CPU becoming a bottleneck. According to a trial calculation, assuming that 1,000 cameras can be accommodated, it is expected that power consumption can be further reduced by 60 percent.” The Japense test project has received a PoC recognition from the IOWN Global Forum.
Chris Wright, chief technology officer and senior vice president of Global Engineering at Red Hat and board director of IOWN Global Forum, commented: “This is important and exciting work, and these results help prove that we can build AI-enabled solutions that are sustainable and innovative for businesses across the globe. With Red Hat OpenShift, we can help NTT provide large-scale AI data analysis in real time and without limitations.”
Katsuhiko Kawazoe, senior executive vice president of NTT and chairman of IOWN Global Forum, said: “This IOWN PoC is an important step forward toward green computing for AI, which supports collective intelligence of AI. We are further improving IOWN’s power efficiency by applying Photonics-Electronics Convergence technologies to a computing infrastructure. We aim to embody the sustainable future of net zero emissions with IOWN.”
