The last KISS seminar addressed the challenges of using large neural models. It presented different techniques for reducing the complexity and the size of a trained model.
The seminar discussed an end-to-end pipeline for model compression. It explained various state-of-the-art compression algorithms, with a focus on quantization and pruning algorithms. Practical use cases using common compression frameworks exemplified typical application scenarios.
By these means, the seminar introduced different compression tools and demonstrated their usability with several practical examples and hands-on exercises.
In more detail, the following topics were addressed:
- Deep Compression: End-to-End compression
- Comparison of different compression methods
- Model Profiling
- Model adaptations for compression
- Compression with Pytorch
- Introduction to Intellabs Distiller
- Introduction to Microsoft Neural Network Intelligence
- Recommendations for best practice
- Comparisons with Compiler Optimizations
- Useful examples
- Hands-on exercises
The seminar was offered as an online seminar using Microsoft Teams as the online meeting platform. Furthermore the practical exercises were performed using virtual machines on Google Cloud Platform (GCP).