Skip to content

Syllabus/Agenda

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
Skip to toolbar