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Syllabus / Agenda

NOTE: agenda is tentative and subject to change 

  • Introduction
    • Embedded AI Definition, Advantages, Challenges, and Applications (Jiang)
  • Core Concepts / Programming Frameworks / Platforms / Tools
    • Fundamentals of Embedded Systems and AI/ML (Jiang)
    • TinyML (Song Han, MIT)
    • Machine Learning Sensors (Vijay Janapa Reddi, Harvard)
    • Hardware AI/ML Accelerators (Zhangxi Tan, RIOS Lab)
    • Embedded Data (Jorge Ortiz, Rutgers)
    • Federated Learning and Mobile AI (Nic Lane, Cambridge)
  • Applications in Embedded AI
    • Acoustic AI (Stephen Xia)
    • AI in Structures (Shijia Pan, UC Merced)
  • Hands-on Tutorials
    • TensorFlow Lite Micro (Jingping Nie, Scott Zhao)
    • Flower (TBD)
    • TVM/uTVM (TBD)
  • Project
    • Your turn!¬†

Weekly Schedule

Date Topic
1/23 Course introduction and logistics
1/30 Fundamentals of Embedded Systems and AI/ML
2/6 Tutorial: TensorFlow Lite Micro
2/13 Hardware AI/ML Accelerators (Zhangxi Tan, RIOS Lab)
2/20 ML Sensors (Vijay Janapa Reddi, Harvard)
2/27 Federated Learning and Mobile AI (Nic Lane, Cambridge)
3/6 FLOWER Tutorial
3/13 Spring Recess
3/20 Acoustic AI (Stephen Xia)
3/27 TinyML (Song Han, MIT)
4/3 Scalable AI
4/10 Tutorial: TVM/uTVM
4/17 Embedded Data (Jorge Ortiz, Rutgers)
4/24 Embedded AI in Structures (Shijia Pan, UC Merced)
5/1 Project demonstrations

 

 

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