Intelligent Devices

  • highly optimized AI engines to analyze text, image, video or time series data
  • algorithms optimization
    • to make the most of the hardware i.e. processing units, accelerators etc.
    • to optimize the usage of precious resources and implementing energy-efficient techniques
  • on edge inferencing to minimize bandwidth needs
  • deployment in the Cloud, Data Center or directly on embedded devices (edge)
  • AI models training in distributed IoT environments (federated learning)

We can advise about selecting optimal configurations, integrate proper baseline components and help build final products on top of that. We know hardware solutions from Xilinx, Intel, NVIDIA, Lenovo, Basler, and many more. We have huge experience in computer vision, machine learning, deep learning and have already delivered a variety of artificial intelligence solutions. Check some of our benchmarks and guidelines when planning to build an intelligent device: Movidius vs. GPU, Cloud vs. on-edge deployment.

Key benefits of deploying AI workloads on the IoT edge devices:

  • Enable Scalability
    (Decentralizes AI services & makes it easier to expand the IoT ecosystems)
  • Enable near-real-time AI experience
    (By using the modern low power, high performance, small form factor accelerators)
  • Solve round-trip latencies
    (Deploying AI directly on the device enables making on-the-spot decisions)
  • Eliminate intermittent connectivity related issues
    (No need for sending the data from the device to external AI services and waiting for results)
  • Reduce costs of bandwidth
    (AI-enabled devices pre-process the data and send the results to external services vs raw data)
  • Data can stay locally on the device
    (Having AI on the device allows for sending the data to external storages selectively)