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Introduction to Embedded AI
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Course Description
Artificial intelligence (AI) is being incorporated into small, low-power embedded computing devices for consumer electronics, industry, and the Internet of Things (IoT). Learn about embedded AI and the technological developments and other factors that are motivating new and exciting embedded AI applications. Explore the devices, signal processing methods, embedded machine learning (ML) algorithms, and embedded ML frameworks that are used to create embedded AI systems. Explore embedded AI use cases such as object recognition, wake word detection, and gesture recognition. Use hands-on examples and activities to explore embedded AI applications that use ML models for on-device processing of audio, motion, and many other signals. Understand the unique challenges posed by the deployment of ML models on resource-constrained devices in TinyML applications. For AI applications in IoT (AIoT), review the benefits of edge computing and the tradeoffs considered when making decisions about whether AI processing is done on the edge device or in the cloud.
Recommended Prerequisite: Basic knowledge of C programming. Note: Students are required to purchase the Arduino Nano 33 BLE board ABX00030 or the Arduino Nano 33 BLE with headers board ABX00034 (The Arduino Nano 33 BLE Sense with headers (ABX00035) or without headers (ABX00031) is preferred if in stock). Some possible options for purchase are https://www.digikey.com
or https://store-usa.arduino.cc
Instructor
Textbook Information
Textbooks for your course may be purchased from any vendor or bookseller of your choice.
Required Textbook(s):
Learning Deep Learning: Theory and Practice of Neural Networks 9780137470358
TinyML: Machine Learning with TensorFlow Lite on Arduino 9781492052043
Meeting Schedule
Event Date Day Start Time End Time Location Room
START 01/09/2023 Monday --- --- Online (Access Begins) ---
END 03/19/2023 Sunday --- --- Online (Access Ends) ---