The Christian Doppler laboratory for Embedded Machine Learning conducts research on Deep Neural Network (DNN) based machine learning in resource constrained embedded devices. It studies the design space that is characterized by DNN architecture parameters, DNN optimization and transformations, various implementation platform configurations, and mapping options. This design space is huge, poorly understood, and rapidly evolving. Our focus is not DNN theory, but DNN implementation under tight cost and energy constraints. The CD lab is organized in three work packages:

  • WP1, Embedded Platforms, assumes a given DNN and study FPGA, GPU, and SoC platforms, and their configuration. It focuses on platform dependant optimization and mapping.
  • WP2, DNN Architecture and Optimization, studies DNN transformations for a given, fixed target platform. Its focus is on platform independent DNN optimization.
  • WP3, Continuous Learning, studies continuous, in-device learning architectures and methods and their implementation and operation on resource constrained embedded devices.

The CD lab conducts world leading research on embedded machine learning in the application domains of computer vision for autonomous systems. For these applications the lab’s objective is to develop world leading architectures and methods with (1) the highest accuracy within a given energy budget, (2) the lowest energy consumption for a given target accuracy, and (3) the ability to do life-long learning in resource constrained environments.

Engineering Trade-offs Engineering Trade-offs
A typical hardware platform A typical hardware platform
The team captured by an articfical neural network. The team captured by an articfical neural network.