Blog Private LTE & 5G Plainsight NVIDIA Edge Computing Mobility Data Center
Blog • • 3 minute read

Enterprise Applications at the Edge with NVIDIA

At the Spring 2022 NVIDIA GPU Technology Conference (GTC), WWT was invited to present alongside NVIDIA's Global Solution Development Director, Joao Gomes, to discuss the latest in hosting low latency graphic intensive applications at the edge with private 5G.

Edge computing is a way for companies in all industry verticals to deliver private mobility services like LTE or 5G alongside demanding enterprise applications that need low latency and high bandwidth infrastructure while communicating with local IoT devices. In the GTC session 5G Edge Monetization with AI and Graphics Enterprise Apps, NVIDIA's Joao Gomes, Kyle Lindsey and I discuss several use cases of graphics technology, along with how organizations can monetize an edge solution. 

An edge computing platform can be located on-premises, at the telecom edge or within a public cloud edge. An organization can choose to adopt one or more of these solution locations and leverage a mobile edge computing (MEC) orchestrator to provide a single pane for managing the environment. This includes supporting hyperscalers from AWS, Google, Azure and more. 

Edge computing is growing at a fast pace, with many independent software vendors (ISVs) being onboarded to support a large ecosystem that has the flexibility to be deployed to the edge and scale based on demand. One edge application use case includes Computer Vision, which uses artificial intelligence (AI) technology to derive information from images and video and complete actions based on the data received. Low latency becomes critical with Computer Vision, as a live camera feed may need an immediate response versus waiting on data being sent to a data center or cloud's inference server. Some of WWT's supported ISVs in the converged edge platform include AICUDA and Plainsight to provide AI analytics at the edge. These two ISVs and many others use NVIDIA's Metropolis Framework for delivering AI to the edge.

Computer Vision example use cases

Computer Vision example use cases

  • Manufacturing
    • Quality control of production lines
    • Inventory Management
    • Shipping/Receiving
  • Sporting Events
    • Crowd management and security
    • Augmented Reality (AR) Integration with live telemetry
    • AR Waypoint directions through stadiums and other venues
  • Healthcare
    • Remote Patient Monitoring (detect patient anomalies through IoT camera)
    • Mixed Reality (XR) training
    • AR-supported surgical procedures and patient diagnosis
    • Overlaying MRIs, X-rays, etc. on patients for accurate imagery
    • AR Waypoint directions through hospitals and clinics
  • Retail
    • AR shopping experiences using automatic shopping carts and geospatial waypoint finding
    • Monitoring food temperatures, inventory, etc.
    • Autonomous stores and customer service robots
  • Entertainment:
    • Provide AR experiences based on guest interests and location tracking
    • Crowd management, security and object detection
  • Finance:
    • Money counting
    • On-premise security and face detection
    • Object detection, license plate/vehicle detection
  • Agriculture:
    • Crop Monitoring
    • Livestock Monitoring and Inventory
    • Machinery Health Monitoring

Another use case discussed during the session includes NVIDIA's CloudXR technology to deliver AR/VR solutions at the edge using NVIDIA GPU hardware. One company designing AR games in the edge is forwARdgame. They've created two exciting sports-related games for us called RaceAR and GolfAR. Both are available through WWT's Advanced Technology Center, and the RaceAR game is being showcased during the NASCAR event at the WWT Raceway in June.

Technologies