AI for computer architecture : principles, practice, and prospects

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Titel
AI for computer architecture principles, practice, and prospects
verantwortlich
Chen, Lizhong (Associate professor) (VerfasserIn); Penney, Drew (VerfasserIn); Jiménez, Daniel (VerfasserIn)
Schriftenreihe
Synthesis lectures on computer architecture ; #55
veröffentlicht
[San Rafael, California]: Morgan & Claypool Publishers, [2021]
Erscheinungsjahr
2021
Teil von
Synthesis lectures in computer architecture ; ; #55.
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Buch
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British Library Catalogue
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245 1 0 |a AI for computer architecture  |b principles, practice, and prospects  |c Lizhong Chen, Drew Penney, Daniel Jiménez 
264 1 |a [San Rafael, California]  |b Morgan & Claypool Publishers  |c [2021] 
300 |a xvii, 124 pages :  |b illustrations ;  |c 26 cm. 
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490 1 |a Synthesis lectures on computer architecture  |x 1935-3232 ;  |v #55 
504 |a Includes bibliographical references. 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 4. Case studies -- 4.1. Supervised learning in branch prediction -- 4.2. Reinforcement learning in NoCs -- 4.3. Unsupervised learning in memory systems. 
505 8 |a 5. Analysis of current practice -- 5.1. Online machine learning applications -- 5.2. Offline machine learning applications -- 5.3. Integrating domain knowledge. 
505 8 |a 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. 
520 |a 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. 
540 |a British Library not licensed to copy  |c 0  |5 Uk 
650 0 |a Computer architecture. 
650 0 |a Artificial intelligence. 
650 0 |a Machine learning. 
650 7 |a Artificial intelligence.  |2 fast  |0 (OCoLC)fst00817247 
650 7 |a Computer architecture.  |2 fast  |0 (OCoLC)fst00872026 
650 7 |a Machine learning.  |2 fast  |0 (OCoLC)fst01004795 
700 1 |a Penney, Drew  |e author. 
700 1 |a Jiménez, Daniel  |d 1969-  |e author. 
830 0 |a Synthesis lectures in computer architecture ;  |v #55. 
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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., 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.
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spelling Chen, Lizhong (Associate professor), author., AI for computer architecture principles, practice, and prospects Lizhong Chen, Drew Penney, Daniel Jiménez, [San Rafael, California] Morgan & Claypool Publishers [2021], xvii, 124 pages : illustrations ; 26 cm., text txt rdacontent, unmediated n rdamedia, volume nc rdacarrier, Synthesis lectures on computer architecture 1935-3232 ; #55, Includes bibliographical references., 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., 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., British Library not licensed to copy 0 Uk, Computer architecture., Artificial intelligence., Machine learning., Artificial intelligence. fast (OCoLC)fst00817247, Computer architecture. fast (OCoLC)fst00872026, Machine learning. fast (OCoLC)fst01004795, Penney, Drew author., Jiménez, Daniel 1969- author., Synthesis lectures in computer architecture ; #55.
spellingShingle Chen, Lizhong (Associate professor), Penney, Drew, Jiménez, Daniel, AI for computer architecture: principles, practice, and prospects, Synthesis lectures in computer architecture, #55, 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., 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., Computer architecture., Artificial intelligence., Machine learning.
title AI for computer architecture: principles, practice, and prospects
title_auth AI for computer architecture principles, practice, and prospects
title_full AI for computer architecture principles, practice, and prospects Lizhong Chen, Drew Penney, Daniel Jiménez
title_fullStr AI for computer architecture principles, practice, and prospects Lizhong Chen, Drew Penney, Daniel Jiménez
title_full_unstemmed AI for computer architecture principles, practice, and prospects Lizhong Chen, Drew Penney, Daniel Jiménez
title_in_hierarchy #55.. AI for computer architecture: principles, practice, and prospects ([2021])
title_short AI for computer architecture
title_sort ai for computer architecture principles practice and prospects
title_sub principles, practice, and prospects
topic Computer architecture., Artificial intelligence., Machine learning.
topic_facet Computer architecture., Artificial intelligence., Machine learning.