Objective

To develop and demonstrate “best practices for embedded AI” by means of four industrial case studies:

  • Embedded Security;
  • Smart Sensors;
  •  Automotive;
  •  Industry 4.0.

Methods

  • Use of FPGAs as accelerators for embedded AI techniques:

    • Adaptability and scalability - the goal is to come up with a concept which would be easily suitable for variety FPGAs manufactured by different vendors. Furthermore, adaptation to the application requirements is also envisioned.

    • Low cost – the industry companies could benefit from the proposed approach only by purchasing affordable FPGA boards that cost only few thousand dollars or less.

  • Dynamic and partial reconfiguration to execute different AI algorithms;
  • End-to-end solution that will provide a complete tool-flow to map from application model to efficient FPGA execution.

Role of BTU Cottbus-Senftenberg (Chair of Computer Engineering)

  • Use of ML as a driver to extend the functionality of NextGen sensors and actuators for Industry 4.0.
  • Build a new class of intelligent devices  able to react to the surrounding environment in order to perform self-calibration, predictive maintenance, self-organization and autonomous control.

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