AI for Computer Architecture : Principles, Practice, and Prospects
Chen, Lizhong (Associate professor)
AI for Computer Architecture : Principles, Practice, and Prospects by Lizhong Chen, Drew Penney, Daniel Jiménez - 1st edition 2021 - 1 recurso en línea (XVII, 124 páginas) - Synthesis Lectures on Computer Architecture 1935-3243 .
Preface -- Acknowledgments -- Introduction -- Basics of Machine Learning in Architecture -- Literature Review -- Case Studies -- Analysis of Current Practice -- Future Directions of AI
obreakspace
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.
9783031017704
10.1007/978-3-031-01770-4 doi
Aprendizaje automático
Arquitectura de ordenador
Inteligencia artificial
QA76.9.A73 / 2021 EB
AI for Computer Architecture : Principles, Practice, and Prospects by Lizhong Chen, Drew Penney, Daniel Jiménez - 1st edition 2021 - 1 recurso en línea (XVII, 124 páginas) - Synthesis Lectures on Computer Architecture 1935-3243 .
Preface -- Acknowledgments -- Introduction -- Basics of Machine Learning in Architecture -- Literature Review -- Case Studies -- Analysis of Current Practice -- Future Directions of AI
obreakspace
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.
9783031017704
10.1007/978-3-031-01770-4 doi
Aprendizaje automático
Arquitectura de ordenador
Inteligencia artificial
QA76.9.A73 / 2021 EB