Administrative information


Title

Model Compression - Edge Computing
Duration 45 mins
Module C
Lesson Type Lecture
Focus Technical - Future AI
Topic Advances in ML models through a HC lens - A result Oriented Study  

 

Keywords


model compression,pruning,quantization,knowledge distillation,

 

Learning Goals


 

Expected Preparation


Learning Events to be Completed Before

Obligatory for Students

  • Knowledge of the supervised learning theory
  • Introduction to machine learning and deep learning concepts given by previous lectures

Optional for Students

  • Knowledge of the most common hyper parameters involved in neural networks building process

References and background for students

Recommended for Teachers

 

Lesson materials


The materials of this learning event are available under CC BY-NC-SA 4.0.

 

Instructions for Teachers


The lecture can refer to model types, model evaluation, model fitting and model optimization

 

Outline

 


Duration Description Concepts Activity
0-10 min Introduction to techniques for model compression: what it is, what it is for, when and why it is needed Model compression Introduction to main concepts
10-20 min Pruning: concepts and techniques. Main approaches to pruning Pruning Taught session and examples
20-30 min Quantization: concepts and techniques. Main approaches to quantization Quantization Taught session and examples
30-40 min Knowledge distillation: concepts and techniques. Main approaches to knowledge distillation Knowledge distillation Taught session and examples
40-45 min Conclusion, questions and answers Summary Conclusions

 

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

 

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

HCAIM Consortium