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Explore embedded AI by considering embedded AI applications, technologies, tools, and current trends in depth. Begin by reviewing applications that use AI to process video, audio, motion, and other signals. Review the details of the embedded ML models (including deep learning neural networks) behind some important applications. Study the highly specialized embedded ML software frameworks that are used to create intelligent embedded systems. Use some of these frameworks in hands-on explorations. Review the steps required to develop and deploy embedded ML models. See specialized devices for embedded AI, including new low power microcontrollers which incorporate neural network accelerators that will enable new battery-powered applications to execute complex ML models. Review the relevance of explainable AI and ethical AI for embedded AI. Conclude by exploring current trends and what may be on the horizon for embedded AI.
Prerequisite: EECS X480.1 Introduction to Embedded AI. Required hardware: Jetson Nano Developer 4G Kit.
Thomas C. Jannett, M.S., Ph.D., is Professor Emeritus of Electrical and Computer Engineering at The University of Alabama at Birmingham. He has more than 30 years of experience in teaching and research. Research expertise includes smart sensor networks that use intelligent control and signal processing. He developed instruments and rehabilitation devices that use embedded microcomputers for data acquisition, signal processing, and intelligent control. He has published extensively in these areas. Current interests include analytics, machine learning, and the Internet of Things.
Textbooks for your course may be purchased from any vendor or bookseller of your choice.
Learning Deep Learning: Theory and Practice of Neural Networks, Comput
Book - ISBN: 9780137470358
Magnus Ekman, 1 ed, Addison-Wesley Professional
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