Frugal Artificial Intelligence in Resource-limited environments

Frugal Artificial Intelligence in Resource-limited environments

Artificial intelligence (AI) is finding increasingly diverse applications in the physical world, especially in embedded, cyber-physical devices with limited resources and under demanding conditions. This type of AI is referred to as “Frugal AI” and is characterised by low memory requirements, reduced computing power and the use of less data. The FAIRe (Frugal Artificial Intelligence in Resource-limited environments) project of DFKI and the French computer science institute Inria is developing a comprehensive approach for all abstraction layers of AI applications at the edge.

Edge devices such as driver assistance and infotainment systems in cars, medical devices, manufacturing or service robots and mobile phones have nowhere near the resources of huge cloud data centres that modern machine learning applications require. The challenge is to deal with limited computing power, limited storage space and limited power consumption.

FAIRe aims to enable the deployment of AI applications on mobile devices through an innovative approach to reduce model size and computational overhead by quantising the network, optimising the network architecture, optimising the computations and finally executing on specialised hardware (e.g. RISC-V based or FPGAs).

This combines the expertise from several DFKI research areas: the actual AI algorithms, the hardware on which they run and the compiler layer in between, which translates AI algorithms as efficiently as possible for a specific hardware. To demonstrate this approach in practice, the project team led by Prof Dr Christoph Lüth is conducting a case study on human-robot interaction (HRI) that covers all of these aspects.

Edge AI projects such as FAIRe contribute to making AI applications widely usable on mobile devices and open up new potential for applications.


  • Inria Taran
  • Inria Cash
  • Inria Corse


Prof. Dr. Christoph Lüth