Resources
Essential Books
Machine Learning Systems
Introduction to Machine Learning Systems by Vijay Janapa Reddi
- Why: Provides an understanding of how ML systems work
- Focus: Chapters on hardware-software co-design and system optimization
- Connection: Essential background for understanding AI-driven architecture design
Deep Learning Fundamentals
Understanding Deep Learning by Simon J.D. Prince
- Why: Solid mathematical foundations for understanding AI techniques used in architecture
- Focus: Optimization methods, neural networks, and learning theory
- Connection: Background for papers using neural approaches to architecture problems
Computer Architecture
Computer Architecture: A Quantitative Approach by Hennessy & Patterson
- Why: The definitive reference for architecture fundamentals
- Focus: Performance analysis, memory hierarchy, instruction-level parallelism
- Connection: Required background for understanding what AI agents are trying to optimize
Computer Organization and Design by Patterson & Hennessy
- Why: More accessible introduction to architecture concepts
- Use: Reference for students needing architecture review
Required Materials
Course Readings
- All readings provided via course website and schedule
- Papers available through Harvard Library databases (see below)
Technology Requirements
- Laptop for hands-on activities and presentations
- Access to Harvard network for database access
Library Access
- Harvard Library: Primary access point for all resources
- ACM Digital Library (via Harvard): ISCA, MICRO, ASPLOS, and other architecture conferences
- IEEE Xplore (via Harvard): DAC, ICCAD, HPCA, and engineering conferences
- Harvard Library Research Guides: Subject-specific research assistance
Getting Help
- Cabot Science Library: Physical location for computer science resources
- Research Consultation: One-on-one help with research strategies
Research Tools & Paper Access
Research Platforms
- ArchGym: RL environment for architecture design space exploration
- DREAMPlace: GPU-accelerated placement engine using deep learning
- CompilerGym: RL environments for compiler optimization
Datasets & Knowledge Bases
- QuArch.ai: Question answering dataset for computer architecture - comprehensive Q&A resource covering architecture concepts, design principles, and system optimization topics
Paper Sources
- ACM Digital Library: ISCA, MICRO, ASPLOS papers
- IEEE Xplore: DAC, ICCAD, HPCA papers
- arXiv: Latest preprints in cs.AR and cs.LG
Paper Discovery
- Semantic Scholar: AI-powered search with citation analysis
- Connected Papers: Visual exploration of paper relationships