Method
Flexible
Term
WINTER
Units
2.0 QUARTER UNITS
Cost
$740

Skills you will gain

  • AI Fundamentals & Neural Networks: Understand the core principles of AI and neural networks for embedded systems.
  • Advanced Packaging Techniques – Explore architectures, materials, and fabrication processes.
  • Physical Verification & EMIR – Learn chip stacking verification and power integrity analysis.
  • Industry Trends & Innovations – Stay ahead with emerging technologies in 3D IC packaging.
  • Collaboration & Practical Application – Work in multidisciplinary teams on real-world projects.

Course Description


Edge AI is revolutionizing embedded systems by bringing powerful machine learning capabilities to low-power devices. This hands-on course explores how AI can be deployed for visual AI (object detection with a camera), audio AI (keyword and command detection), and even lightweight large language models (LLMs) for natural language processing.

Designed for aspiring engineers and developers, students will explore how to leverage AI to enhance the capabilities of embedded hardware like low-power MCU-based systems on a chip ESP32-S3 by Expressif. Over eight sessions, participants will learn the fundamentals of AI, build and optimize neural networks, and deploy custom AI models for real-world applications.

With a strong emphasis on practical application, this course blends in-class exercises, homework assignments, and project-based learning to ensure a deep understanding of AI's potential in embedded systems. Whether you're new to AI or looking to enhance your expertise, this course offers the tools and knowledge to innovate in the rapidly growing field of embedded AI.

 

Learning Outcomes
At the conclusion of the course, you should be able to:

  • Explain fundamental AI concepts and neural network principles.
  • Implement the AI applications using tools like TensorFlow Lite and Edge Impulse on OpenMV.
  • Train and optimize models for embedded systems, focusing on performance and efficiency.
  • Develop a capstone project that integrates AI into a practical embedded solution.

Prerequisites / Skills Needed

 

You will need programming experience in Python.

 

Syllabus Library
Juergen Kienhoefer
Juergen Kienhoefer
Jan. 23 - Mar. 13, Friday, 6:00pm - 9:00pm
  • Flexible Attend in person or via Zoom at scheduled times.
EMBD.X424.(3)
Schedule
Date Start Time End Time Meeting Type Location
Fri, 01-23-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
Fri, 01-30-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
Fri, 02-06-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
Fri, 02-13-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
Fri, 02-20-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
Fri, 02-27-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
Fri, 03-06-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
Fri, 03-13-2026 6:00pm 9:00pm Flexible SANTA CLARA / REMOTE
 

This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each class meeting.

To see all meeting dates, click "Full Schedule" below.

Electronic Course Materials: You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.

Required Tools & Materials:

Development Board

Edition: OpenMV AE3 

https://openmv.io/collections/cameras

An Edge AI device is required to follow this course, approximate cost $100.  Choose either the OpenMV N6 or the OpenMV AE3.  Both are capable of running advanced models such as YOLO.

 


Edition: OpenMV AE3


Authors: Arduino


Publisher: OpenMV


ISBN: https://openmv.io/collections/cameras



An Edge AI device is required to follow this course, approximate cost $100:

Chose either the OpenMV N6 or the OpenMV AE3. Both are capable of running advanced models such as YOLO.

Edition: OpenMV AE3


Authors: Arduino


Publisher: OpenMV


ISBN: https://openmv.io/collections/cameras



An Edge AI device is required to follow this course, approximate cost $100:

Chose either the OpenMV N6 or the OpenMV AE3. Both are capable of running advanced models such as YOLO.

This course applies to these programs:

Demo