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LEADER |
03907cam a22005297i 4500 |
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181-020251065 |
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20230924065657.0 |
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201127s2021 caua b 000 0 eng d |
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100 |
1 |
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|a Chen, Lizhong
|c (Associate professor),
|e author.
|
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.
|
336 |
|
|
|a text
|b txt
|2 rdacontent
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337 |
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|a unmediated
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490 |
1 |
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|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.
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650 |
|
7 |
|a Artificial intelligence.
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650 |
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7 |
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650 |
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7 |
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700 |
1 |
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|a Penney, Drew
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700 |
1 |
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|a Jiménez, Daniel
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|a Synthesis lectures in computer architecture ;
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980 |
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1778756079047409664 |
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Local Holdings |
author |
Chen, Lizhong (Associate professor), Penney, Drew, Jiménez, Daniel |
author_facet |
Chen, Lizhong (Associate professor), Penney, Drew, Jiménez, Daniel |
author_role |
aut, aut, aut |
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Chen, Lizhong (Associate professor) |
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Library A |
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Q - Science |
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QA76 |
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QA76.9.A73 C44 2021 |
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QA76.9.A73 C44 2021 |
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QA 276.9 A73 C44 42021 |
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sid-181-col-blfidbbi |
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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(OCoLC)1224299893 |
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000 - Computer science, information & general works |
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006.3 |
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16.3 |
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000 - Computer science, knowledge & systems |
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Book |
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181-020251065 |
illustrated |
Illustrated |
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[San Rafael, California], Morgan & Claypool Publishers, [2021] |
imprint_str_mv |
[San Rafael, California] Morgan & Claypool Publishers [2021] |
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FID-BBI-DE-23 |
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|
is_hierarchy_title |
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9781681739847, 1681739844, 9781681739861, 1681739860 |
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9781681739854 |
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FID-BBI-DE-23 |
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1935-3232 ; |
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English |
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2023-10-03T17:26:26.84Z |
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British Library Catalogue |
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1224299893 |
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xvii, 124 pages; illustrations; 26 cm |
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[2021] |
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2021 |
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[San Rafael, California] |
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Morgan & Claypool Publishers |
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Synthesis lectures in computer architecture, #55 |
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Synthesis lectures on computer architecture ; #55 |
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181 |
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. |