Difference between revisions of "Probing the limits of fake-quantised neural networks"
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+ | * Fundamental concepts of deep learning (convolutional neural networks, backpropagation, computational graphs) | ||
* Numerical representation formats (integer, floating-point) | * Numerical representation formats (integer, floating-point) | ||
* Numerical analysis | * Numerical analysis |
Revision as of 20:40, 24 September 2021
Contents
Introduction
Abstract of the project
Project description
Competences
Required:
- Fundamental concepts of deep learning (convolutional neural networks, backpropagation, computational graphs)
- Numerical representation formats (integer, floating-point)
- Numerical analysis
- Python programming
- C/C++ programming
Optional:
- Knowledge of the PyTorch deep learning framework
- Knowledge of digital arithmetic (e.g., two's complement, overflow, wraparound)
Character
- 20% Theory
- 40% C/C++ and Python coding
- 40% Deep learning
Professor
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 Spallanzanispmatteo@iis.ee.ethz.ch, Renzo Andri (Huawei RC Zurich)
Logistics
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.