Hardware/Software Co-Design for Machine Learning

Catalog description

The course is designed to introduce an engineering discipline at the intersection of machine learning and hardware systems to fill the gap. The covered topics include basics of deep learning, deep learning frameworks, deep learning on contemporary computing platforms (CPU, GPU, FPGA) and programmable accelerators (TPU), performance measures, numerical representation and customized data types for deep learning, co-optimization of deep learning algorithms, software and hardware, training for deep learning and complex deep learning models. The course is structured with a combination of lectures, labs, research paper reading/in-class discussion, a final project and guest lectures with state-of-the-art industry practices.