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Exploring schedules for incremental and annealing quantization algorithms

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Introduction

Project description

References

Competences

Required:

  • Algorithms & data structures
  • Python programming
  • Basic knowledge in deep learning (convolutional neural networks, backpropagation)

Useful:

  • Knowledge of the PyTorch deep learning framework
  • C/C++ programming


Professor

Luca Benini


Status: Available

Possible to complete as a Semester Thesis or as a Master Thesis.

Supervision: Matteo Spallanzani spmatteo@iis.ee.ethz.ch


Logistics

The student(s) 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 15 minutes talk in front of the IIS team, and defend it during the following 5 minutes discussion.