In recent years, the metrics used for optimizing and evaluating AI algorithms are diversifying: along with accuracy, there is increasing emphasis on the metrics such as energy efficiency and model size. Given this, researchers working on deep learning can no longer afford to ignore the computing system. Instead, the knowledge of the potential and limitations of computing systems can provide invaluable guidance to them in designing the most efficient and accurate algorithms. This course aims to inform students, practitioners and researchers in deep-learning algorithms about the potential and limitations of various processor architectures for accelerating the deep learning algorithms. At the same time, it seeks to motivate and even challenge the engineers and professionals in the architecture domain to optimize the processors according to the needs of deep-learning algorithms.