NOTE: agenda is tentative and subject to change
- Introduction
- Embedded AI Definition, Advantages, Challenges, and Applications
- Core Concepts / Programming Frameworks / Platforms / Tools
- Fundamentals of Embedded Systems and AI/ML
- TinyML
- Machine Learning Sensors
- Hardware AI/ML Accelerators
- Embedded Data
- Federated Learning and Mobile AI
- Generative AI on the Edge
- Applications in Embedded AI
- Acoustic AI
- Robotic AI
- Physical Knowledge-Informed AI
- Hands-on Tutorials
- TinyML Image Classification, Object Detection, Keyword Spotting (Xiao ESP32S3 Sense)
- LLM/VLM on the edge (Raspberry Pi)
- Federated Learning (Flower)
- Project
- Your turn!
Date | Topic |
---|---|
1/27 | Course introduction and logistics (Fred Jiang) |
2/3 | Fundamentals of Embedded Systems and AI/ML (Fred Jiang) |
2/10 | TinyML (Tutorial 1) |
2/17 | Benchmarking AI (Vijay Janapa Reddi) |
2/24 | Hardware AI/ML Accelerators (Zhangxi Tan) |
3/3 | Efficient LLM (Guangxuan Xiao) |
3/10 | Federated Learning (Nic Lane) |
3/17 | Spring Recess |
3/24 | Edge AI (Mi Zhang) |
3/31 | LLM/VLM on the edge (Tutorial 2) |
4/7 | Federated Learning (Tutorial 3) |
4/14 | Multi-modal Models (Jorge Ortiz) |
4/21 | Sensor LLM (Bashima Islam) |
4/28 | Acoustic AI (Stephen Xia) |
5/5 | Project Demonstrations |