The Machine Learning for Embedded Systems Graduate Certificate program will equip students with the skills needed to harness the power of machine learning within computing systems, such as those in autonomous vehicles, medical devices, and smart appliances.
“As machine learning models become more complex, their energy consumption and demand for computational resources grow,” said Maryam Parsa, an assistant professor of electrical and computer engineering, who will be teaching in the program. “By focusing on embedded [computer] systems, this program contributes to the development of energy-efficient machine learning implementations, crucial for reducing the carbon footprint of digital technologies.”
In addition, Parsa noted, that the program will address the importance of data privacy and security in embedded systems.
With electives in neuromorphic computing, big data technologies, and hardware accelerators for machine learning, among other topics, this program prepares alumni of computer or electrical engineering students to tackle complex machine learning challenges. Parsa added the program would be appropriate for “embedded software developers writing code for microcontrollers” and “hardware designers who do not have a background in machine learning.”
With a diverse range of new offerings, Mason continues to lead the way in engineering education and research.