Courses
Lecture: TuTh 3:00pm-4:15pm
Facility: GY 4069
This course is a graduate-level course in computer architecture with special topics in hardware acceleration for machine learning. Advanced undergraduates who have fulfilled the prerequisites are welcome to enroll. This course provides an essential background in the training and inference of deep neural networks (DNNs), deep learning frameworks, and hardware accelerators. This course surveys the recent trends that reduce the computation, storage, and communication cost of DNNs via co-optimization of algorithms and hardware.
Students are expected to read, present, and interact with research papers, and complete three course projects and one final take-home exam. Prerequisites include introductory Computer Organization and Digital Logical Design.
Course materials, labs, and readings are available through Canvas.Lecture: MW 11:30am-12:45pm
Facility: IF 4012
This course is a graduate-level course in computer architecture with special topics in hardware acceleration for machine learning. Advanced undergraduates who have fulfilled the prerequisites are welcome to enroll. This course provides an essential background in the training and inference of deep neural networks (DNNs), deep learning frameworks, and hardware accelerators. This course surveys the recent trends that reduce the computation, storage, and communication cost of DNNs via co-optimization of algorithms and hardware.
Students are expected to read, present, and interact with research papers, and complete three course projects and one final take-home exam. Prerequisites include introductory Computer Organization and Digital Logical Design.
Course materials, labs, and readings are available through Canvas.Lecture: MW 11:30am-12:45pm
Facility: IF 4063
This course is a graduate-level course in computer architecture with special topics in hardware acceleration for machine learning. Advanced undergraduates who have fulfilled the prerequisites are welcome to enroll. This course provides an essential background in the training and inference of deep neural networks (DNNs), deep learning frameworks, and hardware accelerators. This course surveys the recent trends that reduce the computation, storage, and communication cost of DNNs via co-optimization of algorithms and hardware. Students are expected to read, present, and interact with research papers, and complete three course projects. Prerequisites include introductory Computer Organization and Digital Logical Design.
Course materials, labs, and readings are available through Canvas.Lecture: October 18 11:15am-12:05pm
Facility: IF 4012
This course provides an introduction to Intelligent Systems Engineering and an overview of the various degree specializations that are available. ISE is a set of modern Systems Engineering areas with various interrelations. This course provides a broad introduction and details of faculty research areas.
Course materials, labs, and readings are available through Canvas.Lecture: MW 1:10pm-2:25pm
Lab: F 1:45pm-2:35pm
Facility: GY 4068
This course is an introduction to circuits and linear analysis. Topics include voltage and current sources, Kirchhoff's laws, Ohm's law, Nodal and Mesh analysis, Thevenin and Norton equivalent circuits, operational amplifiers, inductors and capacitors, frequency analysis of first-order and second-order systems, simple filter designs, and power dissipation calculations.
Students will learn topics by listening to lectures and participating in discussions and from reading the textbook and supplementary readings. A series of hands-on laboratory exercises and a significant team-oriented design project will provide students with an opportunity to apply and explore the material.
Course materials, labs, and readings are available through Canvas.Lecture: MW 11:30am-12:45pm
Virtual
This course is a graduate-level course in computer architecture with special topics in hardware acceleration for machine learning. Advanced undergraduates who have fulfilled the prerequisites are welcome to enroll. This course provides an essential background in the training and inference of deep neural networks (DNNs), deep learning frameworks, and hardware accelerators. This course surveys the recent trends that reduce the computation, storage, and communication cost of DNNs via co-optimization of algorithms and hardware. Students are expected to read, present, and interact with research papers, and complete three course projects. Prerequisites include introductory Computer Organization and Digital Logical Design.
Course materials, labs, and readings are available through Canvas.