Screen LibraryAI for computer architecture : principles, practice, and prospects /
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-bas...
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| Main Authors: | |
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| Group Author: | ; |
| Published: |
Morgan & Claypool Publishers,
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| Publisher Address: | [San Rafael, California] : |
| Publication Dates: | [2021] |
| Literature type: | Book |
| Language: | English |
| Series: |
Synthesis lectures in computer architecture ; #55. 1935-3235 |
| Subjects: | |
| Summary: |
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. |
| Carrier Form: | xvii, 124 pages : illustrations (some color) ; 24 cm. |
| Bibliography: | Includes bibliographical references (pages 103-121). |
| ISBN: |
9781681739847 1681739844 9781681739861 1681739860 |
| Index Number: | QA76 |
| CLC: | TP303 |
| Call Number: | TP303/C518-1 |
| Contents: |
1. Introduction -- 1.1. The rise of AI in architecture -- 1.2. The scope of AI -- 1.3. Fundamental applicability -- 1.4. Levels of AI for architecture 2. Basics of machine learning in architecture -- 2.1. Supervised learning -- 2.2. Unsupervised learning -- 2.3. Semi-supervised learning. -- 2.4. Reinforcement learning -- 2.5. Evaluation metrics 3. Literature review -- 3.1. System simulation -- 3.2. GPUs -- 3.3. Memory systems and branch prediction -- 3.4. Networks-on-chip -- 3.5. System-level optimization -- 3.6. Approximate computing 4. Case studies -- 4.1. Supervised learning in branch prediction -- 4.2. Reinforcement learning in NoCs -- 4.3. Unsupervised learning in memory systems 5. Analysis of current practice -- 5.1. Online machine learning applications -- 5.2. Offline machine learning applications -- 5.3. Integrating domain knowledge 6. Future directions of AI for architecture -- 6.1. Investigating models and algorithms -- 6.2. Enhancing implementation strategies -- 6.3. Developing generalized tools -- 6.4. Embracing novel applications -- 7. Conclusions. |