AI for computer architecture : principles, practice, and prospects
Bibliographische Detailangaben
- Titel
- AI for computer architecture principles, practice, and prospects
- verantwortlich
- ; ;
- Schriftenreihe
- Synthesis lectures on computer architecture ; #55
- veröffentlicht
- Erscheinungsjahr
- 2021
- Teil von
- Synthesis lectures in computer architecture ; ; #55.
- Medientyp
- Buch
- Datenquelle
- British Library Catalogue
- Tags
- Tag hinzufügen
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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.