This course covers the hardware design principles to deploy different machine learning algorithms. The emphasis is on understanding the fundamentals of machine learning and hardware architectures and determine plausible methods to bridge them. Topics include precision scaling, approximate computing, in-memory computing, architectural modifications, GPUs, and vector architectures, as well as recent EDA tools for AI such as Xilinx AI Vitis, Xilinx HLS, Tensorflow Lite, and Caffee. Offered by Electrical & Comp. Engineering. May not be repeated for credit.
Hardware Accelerators For Ml
George Mason University
Electrical & Computer Engineering
Weiwen Jiang (email@example.com)
Times and Days
((ECE 511 or CS 465) and (ECE 527 or ECE 554 or CS 580 or CS 688)) or equivalent