Deep Learning is taking the world by storm, but too complex to run on embedded devices.

    • Low Latency
      JumpML Value Proposition
    • Bring magical AI/ML experiences to embedded devices

    • Why bother with AI/ML on Embedded Devices?
      Low-latencyLow Latency
      No Cloud FeeCash
  • Embedded devices are severely constrained in compute capability, size of memory and a complicated path to model deployment.
    • Challenges on Constrained Embedded Devices
      Model DeploymentMemory
  • At JumpML, we combine the simplicity and efficiency of classical digital signal processing (DSP) and the best ML methods to develop high-performance solutions, that are lightweight and energy-efficient.

    Our internal development process involves training models in PyTorch, followed by conversion of the models to C code, that can run efficiently on any embedded system.

    More Information

    Please check out our Products page for more information on our current offerings.

    For technology demos and specific use cases, please check out our Use Cases page.

    For questions and more details, please contact us at JumpML email.

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