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Probing the limits of fake-quantised neural networks

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Deep neural networks usually require huge computational resources to deliver their statistical power. However, in many applications where latency and data privacy are important constraints, it might be necessary to execute these models on resource-constrained computing systems such as embedded, mobile, or edge devices.

Project description

File:Probing the limits of fake-quantised neural networks.pdf

Skills and project character



  • Fundamental concepts of deep learning (convolutional neural networks, backpropagation, computational graphs)
  • Numerical representation formats (integer, floating-point)
  • Numerical analysis
  • Python programming
  • C/C++ programming


  • Knowledge of the PyTorch deep learning framework
  • Knowledge of digital arithmetic (e.g., two's complement, overflow, wraparound)

Project character

  • 20% Theory
  • 40% C/C++ and Python coding
  • 40% Deep learning


The student and the advisor will meet on a weekly basis to check the progress of the project, clarify doubts, and decide the next steps. The schedule of this weekly update meeting will be agreed at the beginning of the project by both parties. Of course, additional meetings can be organised to address urgent issues.

At the end of the project, you will have to present your work during a 20 minutes talk in front of the IIS team and defend it during the following 5 minutes discussion.


Luca Benini

Status: Available

We are looking for 1 Master student. It is possible to complete the project either as a Semester Project or a Master Thesis.

Supervisors: Matteo Spallanzani, Renzo Andri (Huawei RC Zurich)