![](https://moodle.polymtl.ca/pluginfile.php/1051851/course/overviewfiles/e4167cbe-4843-11ec-aba3-fad0c0ca5ea6_1637221919824.jpg)
General-purpose (CPU, GPU) and specialized (systolic, many-core, etc.) hardware architectures for deep learning (DL). Energy consumption calculation of a DL model at different abstraction levels. Optimizing models for implementation: quantization, compression, pruning, and design space exploration. Interactions between DL models and hardware architectures and optimization of hardware accelerator designs. Experimental project focusing on minimizing the energy cost of a deep learning task.
- Responsable du site: François Leduc-Primeau
- Enseignant (éditeur) : Reda Bensaid
- Enseignant (éditeur) : Kamran Chitsaz Zade Allaf