Course Schedule and Catalog
Course Schedule - Fall Semester 2019
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
ELEC 515 001 (CRN: 13290)
EMBEDDED MACHINE LEARNING
Long Title: MACHINE LEARNING FOR RESOURCE-CONSTRAINED PLATFORMS
Department: Electrical & Computer Eng.
Instructor: Lin, Yingyan
Meetings:
9:30AM - 10:45AM W (26-AUG-2019 - 6-DEC-2019)
9:40AM - 10:55AM M (26-AUG-2019 - 6-DEC-2019)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 30
Section Enrolled: 6
Enrollment data as of: 7-FEB-2025 2:32AM
Final Exam: GR Course-Dept Schedules Exam
Description: Machine learning is in tremendous demand in numerous applications; however, its often prohibitive complexity remains a major challenge for its extensive deployment in resource constrained platforms. This course will introduce techniques which enable the development of energy/time efficient machine learning systems, taking a path from algorithm to architecture down to the circuit level. In particular, you will first learn commonly used machine learning algorithms, and then algorithm-, architecture-, circuit-level techniques for reducing the energy/time cost of machine learning systems while maintaining their powerful performance. Finally, we will do a deep dive into state-of-the-art efficient machine learning systems, such as Google's TPU and Eyeriss.