AI for computer architecture : principles, practice, and prospects

Bibliographische Detailangaben

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.
Medientyp
Buch
Datenquelle
British Library Catalogue
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Inhaltsangabe:
  • 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.