Nvidia has shown off its new EGX platform, which aims to bring AI-powered computing to the network edge. But there’s been little fanfare in the press about the announcement, and the hype for both AI and edge developments is dying down.
Nvidia is pitching EGX at companies that need to perform low latency AI processing at the network edge. It cites continuous data streaming between 5G base stations as one use case, as well as warehouses, retail stores, and factories, saying that EGX was built “to meet the growing demand to perform instantaneous, high throughput AI at the edge, where data is created, with guaranteed response times, while reducing the amount of data that must be sent to the cloud”.
As for the size of this market, Nvidia is using the somewhat nebulous term “machine sensors”, saying that there will be 150bn of these and IoT sensors by 2025 (according to a Seagate-sponsored IDC report), which will collectively stream continuous data that will need to be sifted.
Nvidia EGX is an offering that ranges from just one Jetson Nano, which was unveiled back in March, which might be found in a device consuming a few watts of electricity, all the way up to a rack of Nvidia T4 servers. This spans a range of 0.5 TOPS all the way through to a claimed 10,000 TOPS, from an application like image recognition all the way to real time speech recognition and similar AI tasks, says Nvidia.
The Nvidia silicon is one component, but its new Edge Stack is equally important. Deployed using Kubernetes, for that sweet container management flexibility, Nvidia has also partnered with Red Hat (IBM), to integrate this stack with OpenShift – one of the leading Kubernetes orchestration platforms. Cisco is also a launch partner, and Nvidia’s recent Mellanox purchase crops up, by way of the option to install Mellanox Smart NICs in your box.
As for the list of server providers, it currently reads: Atos, Cisco, Dell EMC, Fujitsu, HPE, Inspur and Lenovo, Abaco, Acer, Adlink, Advantech, ASRock Rack, ASUS, AverMedia, Cloudian, Connect Tech, Curtiss-Wright, Gigabyte, Leetop, MiiVii, Musashi Seimitsu, QCT, Sugon, Supermicro, Tyan, WiBase and Wiwynn.
Nvidia points to video analytics applications as being ideal for retail and smart city applications, with options available from AnyVision, DeepVision, IronYun and Malong Technologies. Some unspecified healthcare-focused software offerings are also cited, from 12 Sigma, Infervision, Qunatib and Subtle Medical.
Nvidia does say that there are over 40 early adopters, which include BMW Group Logistics, which is using EGX and Nvidia’s Isaac robotics platform, and Foxconn, which is using EGX to power quality control inspection in its PC production line, with an apparent 40% increase in throughput.
Also namedropped was GE Healthcare, which deploys Nvidia T4 GPUs into its magnetic resonance (MR) systems and its Edison Intelligence platform, and Seagate crops up again, which like Foxconn, is using EGX to power quality inspection on its hard drive production lines.
“At Seagate we have deployed an intelligent edge GPU-based vision solution in our manufacturing plants to inspect the quality of our hard disk read-and-write heads. The NVIDIA EGX platform dramatically accelerates inference at the edge, allowing us to see subtle defects that human operators haven’t been able to see in the past. We expect to realize up to a 10 percent improvement in manufacturing throughput and up to 300 percent ROI from improved efficiency and better quality,” said Bruce King, senior principal data scientist at the company.