6.S082/6.888 Hardware Architecture for Deep Learning

SHARE:

Undergraduate AUS /Graduate
Prerequisites 6.003, 6.004
Units: 3-3-6
Instructors:  Professors Vivienne Sze and Joel Emer
Schedule:  MW11-12:30, room 32-144
 
Description
This subject will count as an AUS subject and for the graduate level as a Computer Systems concentration subject.
 
Introduction to the design and implementation of hardware architectures for efficient processing of  deep learning algorithms in AI systems. Topics include basics of deep learning, deep learning on
programmable platforms, deep learning accelerators, co-optimization of deep learning algorithms and hardware, training for deep learning, support for complex deep learning networks, applications of
advanced technologies for deep learning. Includes labs involving modeling and analyzing deep learning hardware architectures, building systems using popular deep learning tools and platforms
(CPU, GPU, FPGA) and an open-ended design project.