Units:

12

Description:

Fall 2024 Description:

Embedded or “edge” devices with sensors generate a tremendous amount of data every second. Sending these data to the cloud for intelligent decision making by machine learning models consumes energy and imposes undesired latency and cost. Processing the data locally on the edge lowers latency, energy, and cost. This course introduces deep neural network architectures, such as dense, convolutional, and recurrent networks, and their respective applications and training in the cloud. Students then learn to downsize their trained models so they can deploy them for inferencing on microcontrollers running on the edge with power and computation constraints. Students are encouraged to create their own projects drawing from such fields as agriculture, environment, conservation, health, manufacturing, or home automation. Students are expected to have embedded systems knowledge equivalent to 18-349 (Introduction to Embedded Systems). This course is cross-listed as 18-448C and 18-848C. Although students in 18-448C and 18-848C will share lectures, students in 18-448C and 18-848C will receive different homework assignments, design projects, and exams.

Last Modified: 2024-08-09 2:04PM

Current session:

This course is currently being offered.

Semesters offered:

• Fall 2024
• Spring 2024
• Fall 2023
• Spring 2023