Scalable Deployments at the Edge

ASSET-Rx EDGE enables the rapid design, development, and massively scalable deployment of real-time sensing, control, and machine vision applications at the edge. It is the platform of choice for connecting the physical world to business and enterprise applications.

The platform runs on edge computers, transforming them into easy to deploy and easy to maintain Edge Processing Units in a broad range of use cases and applications:

  • Machine vision, image, and video processing
  • Heterogeneous wired and wireless sensing
  • Machine health in manufacturing and military
  • Manufacturing test systems in biopharma, automotive, EMS
  • Residential and commercial energy management
  • Security and employee safety
  • Visual inspection and anomaly detection
  • Portable or stationary robotics applications

If your success depends on the fast deployment of machine vision, analytics, AI, sensor data collection, and IIoT applications uniquely crafted for each of your edge devices over a uniform platform, we suggest adopting ASSET-Rx EDGE. Cloud Orchestrated from ASSET-Rx EDGE SYNC, the platform transforms industrial computers into multi-cloud connected Edge Processing Units for heterogeneous sensing applications.

ARM-Based Systems Covered

ASSET-Rx EDGE Operates on High-Performance multi-core, GPU-enabled ARM-based edge processing hardware, in addition to x86 systems.

Mix and Match Your Existing or New Hardware

ASSET-Rx EDGE deployment accommodates the use of your existing hardware components such as the integrated sensors in your machines, existing security cameras. It creates the pathway to scale the intelligence of your systems by adding new peripherals to your deployment as your operations and operational needs scale.

ASSET-Rx EDGE is a scalable platform for the development and deployment of applications at the edge, especially for machine vision and data acquisition applications.  ASSET-Rx EDGE provides a component architecture, low-code tools for development and deployment, and a library of building blocks ranging from configurable production apps to example applications.  Although our technology is general, we focus on the following areas:

  • Support for edge devices running Linux: Edge devices, including true embedded devices, now run mainstream Linux distributions, containing Docker, containing support for many IO channels, and providing significant compute and communications power.  Virtually any sensor or image capture device can be connected to a Linux system.
  • Machine vision/image processing: We focus on the acquisition of high quality, high-speed images in the visual and IR ranges, and on the acquisition of data from “vision-like” systems such as LIDAR.  Specifically, we provide configurable components for the acquisition of images and video.  We also support the acquisition of data from the other sensors found on these nodes: temperature, accelerometer, etc.
  • Local inferencing and decision capabilities: Connectors from cameras and other sensors directly to ML inferencing in the local machine, and to decision-making tools that use the output of the ML inferencing.  These tools provide alarms, support for process control and command, etc.
  • Connectors to the cloud: Communications to services via RESTful transactions, MQTT, and other standard protocols.
  • Scalable deployment: The end product of an ASSET-Rx EDGE development cycle is a componentized application package that can be installed on 1 machine, 10 machines, or 1000 machines.   This is a key advantage for system resellers and integrators.

Our framework, while providing scalability in deployment for edge computers, is compatible with, not competitive with, the various platforms that provide compute and storage scalability over the network and in the cloud.  We use Docker extensively, and individual components in an ASSET-Rx EDGE application package provide entry points into Hadoop, Flink, Spark, and similar frameworks that normally reside in the cloud.  One way to think of it is that ASSET-Rx EDGE scales up the machinery to feed data to those systems.