Pruning and Quantization for Edge AI | TinyML Practical Session
Автор: EmbedSystems
Загружено: 2026-02-02
Просмотров: 17
Описание:
This video is a recording of the second session from our TinyML seminar at Mälardalen University (MDU), focused on model pruning and quantization for embedded and edge AI systems.
⚠️ Note: The recording did not capture the very beginning of the session due to a late start. However, the core technical content, explanations, and live demonstrations are fully intact and provide strong practical value.
In this session, we cover:
• Motivation for model pruning in TinyML
• Structured vs unstructured pruning concepts
• Accuracy–efficiency trade-offs
• Practical pruning workflows
• Live notebook demonstrations on quantization and pruning
• Combining pruning and quantization for efficient deployment
The session emphasizes hands-on understanding, showing how compression techniques can be applied in practice to reduce model size, computation, and energy consumption for resource-constrained devices.
This lecture is intended for students, researchers, and engineers working in embedded systems, edge AI, and TinyML.
📂 Seminar materials and notebooks:
https://github.com/HERO-MDH/TinyML-Se...
▶️ Part of the TinyML Seminar @ MDU playlist.
Повторяем попытку...
Доступные форматы для скачивания:
Скачать видео
-
Информация по загрузке:
