AI for Computer Architecture: Principles, Practice, and Prospects

By by Lizhong Chen (Author), Drew Penney (Author), Daniel Jiménez (Author)

Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs

Topics
Computer architecture,
Artificial intelligence,
Machine learning,
Automated architecture design,
Design space exploration,
Design optimization,
Supervised learning,
Unsupervised learning,
Semi-supervised learning,
Reinforcement,