The course explores in detail aspects of the hardware that underpins machine learning systems today. It discussed the computational requirements of machine learning models and how different hardware features support this. It explains how various architectural aspects have been developed to improve the performance of machine learning applications, and the resulting interplay between model accuracy, computing latency, and energy efficiency. It discusses the mapping of different models to a variety of different hardware platforms and emerging hardware approaches for machine learning.