ECE 6790

ECE 6790

Course information provided by the Courses of Study 2023-2024.

Industry and academia have shown large interest in low-power hardware designs for neuromorphic computing (e.g. IBM TrueNorth, Intel Loihi) and deep learning algorithms for a wide range of image, speech, and biomedical applications. In this course, we will learn the underlying theory, basic algorithms, and efficient circuit/architecture design of neuromorphic computing.

When Offered Spring.

Comments This course requires the student to be comfortable with either Matlab/Python languages for algorithm development, or RTL coding and computer-aided design (CAD) tool based digital circuit design.

Outcomes
  • Gain an understanding on neuromorphic computing and neural networks.
  • Learn how to design energy-efficient hardware accelerators for neuromorphic computing and neural networks.
  • Learn how to properly optimize the performance/power/area of neuromorphic hardware designs.

View Enrollment Information

Syllabi:
  •   Regular Academic Session. 

  • 3 Credits GradeNoAud

  • 19603 ECE 6790   LEC 001

  • Instruction Mode: Distance Learning-Synchronous
    Limited to PhD students.

Syllabi:
  •   Regular Academic Session. 

  • 3 Credits GradeNoAud

  • 19608 ECE 6790   LEC 030

  • Instruction Mode: In Person
    Taught in NYC at Cornell Tech. Enrollment Limited to Cornell Tech PhD Students only. Tech Master's may seek approval by contacting the faculty.