A survey of machine learning for computer architecture and systems.

Machine learning has increasingly become relevant in the field of computer architecture and systems. It involves training machines to learn from data and make decisions or predictions based on patterns found in that data. In the context of computer architecture, machine learning techniques are applied to enhance the performance, efficiency, and security of systems.

Machine learning algorithms can be used to optimize various aspects of computer systems, such as improving memory management, enhancing processor design, and enhancing the overall system performance. By analyzing data and identifying patterns, machine learning can help in making more informed decisions in designing and managing computer systems.

Researchers and engineers in the field of computer architecture are exploring how machine learning can be leveraged to tackle complex challenges in system design, such as reducing power consumption, speeding up computations, and enhancing security measures. By integrating machine learning into computer architecture and systems, we can expect to see significant advancements in the efficiency and intelligence of computing devices.