The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.

Machine learning has emerged to be a key approach to solving complex cognition and learning problems. Deep neural networks, in particular, have become pervasive due to their successes across a variety of applications, including computer vision, speech recognition, natural language processing, etc. While machine learning algorithms deliver impressive accuracy on many deployment scenarios, the complexity of the algorithms also poses a unique computational challenge to state-of-the-art hardware design. To this end, this course is designed to help students come up to speed on various aspects of hardware for machine learning, including basics of deep learning, deep learning frameworks, hardware accelerators, co-optimization of algorithms and hardware, training and inference, support for state-of-the-art deep learning networks. In particular, this course is structured around building hardware prototypes for machine learning systems using state-of-the-art platforms (e.g., FPGAs and ASICs). It's also a seminar-style course so students are expected to present, discuss, and interact with research papers. At the end of the semester, students will present their work based on a class research project.

Prerequisites: EECS151/251A

Requisites

• Graduate students NOT in the Master of Engineering Program other those in EECS

Repeat Rules