Carnegie Mellon University

Electrical and Computer Engineering

College of Engineering

Course Information

18-663SV: Hardware Architectures for Machine Learning




Machine learning is poised to change the landscape of computing in more ways than its broad societal applications. Indeed, hardware architectures that can efficiently run machine learning face increasing challenges due to power consumption or run time constraints that technology, platforms, or users impose. This course provides an overview of current advances in hardware architectures that can enable fast and energy efficient machine learning applications from the edge to the cloud. Topics include hardware accelerators, hardware-software co-design, and general or application specific system design and resource management for machine learning applications.

In Spring 2019 this course is broadcast to the Silicon Valley campus. ECE Silicon Valley students attend classes synchronously with students in Pittsburgh.

Prerequisites: (18-461 OR 18-661 OR 10-401 OR 10-601 OR 10-701) AND (18-447 OR 18-340)

Last Modified: 2018-11-08 3:36PM

Semesters offered:

  • Spring 2019