Backpropagation-free Model Adaptation on IoT Devices

Deep models deployed on edge devices frequently encounter distribution shifts in sensed data. This issue primarily arises from a sensor drift, varying environmental conditions or context, and leads to a discrepancy between the real and the training data distributions. Model re-training using backpropagation is expensive, beside derivate computation, the memory overhead scales linearly with model depth and batch size. Machine learning frameworks for resource-constrained IoT devices therefore optimize model inference and usually do not support on-device model updates. Unfortunately, updating even a small fraction of model parameters still requires a fully-features backward pass. In this project, we aim to support on-device model adaptation by leveraging bio-inspired learning theory. Interested? Please contact us for more details!

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Student Target Groups:

  • Students in ICE
  • Students in Computer Science
  • Students in Software Engineering.

Thesis Type:

  • Master Project / Master Thesis

Goal and Tasks:

The goal of this project is to extend the Tensor- Flow Lite Micro framework by adding the support for runtime fine-tuning of models in response to distribution shifts, utilizing the principles of Hebbian learning theory. The project includes the following tasks:

  • In-depth understanding of standard model retraining using backpropagation;
  • Familiarize yourself with TFL Micro and how it can be expended to support model updates using backpropagation-free methods;
  • Implement the training algorithm and analyze its performance;
  • Summarize the results in a written report.

Recommended Prior Knowledge:

  • Eager to learn about deep neural networks and on-device adaptation on IoT devices;
  • Programming skills in Python;
  • Prior experience with machine learning frameworks (e.g., TensorFlow, PyTorch).


  • a.s.a.p.