Computer Architecture

1. Trends and Future Directions
   1. Emerging Non-Volatile Memory Technologies
      1. Types of Non-Volatile Memory
         1. Ferroelectric RAM (FeRAM)
         2. Magnetoresistive RAM (MRAM)
         3. Phase-Change Memory (PCM)
         4. Resistive RAM (ReRAM)
      2. Advantages over Traditional Memory
         1. Reduced Power Consumption
         2. Faster Data Access
         3. Improved Durability
      3. Applications and Use Cases
         1. High-Speed Data Storage
         2. Mobile Devices
         3. Internet of Things (IoT)
         4. Enterprise Computing
      4. Challenges and Limitations
         1. Cost of Production
         2. Integration with Existing Systems
         3. Data Retention over Long Periods
   2. 3D Chip Architectures
      1. Structure and Design
         1. Vertical Integration of Layers
         2. Through-Silicon Vias (TSVs)
      2. Performance Benefits
         1. Increased Bandwidth
         2. Reduced Latency
         3. Improved Power Efficiency
      3. Manufacturing Challenges
         1. Heat Dissipation
         2. Fabrication Complexity
         3. Yield and Reliability Concerns
      4. Current Applications
         1. Memory Stacking (e.g., HBM, 3D NAND)
         2. Logic-on-Logic Integration
         3. Hybrid Systems (Combining Memory and Logic)
   3. Optical Computing
      1. Principles of Optical Computing
         1. Use of Photons vs. Electrons
         2. Optical Components (e.g., lasers, modulators)
      2. Potential Advantages
         1. Higher Bandwidth
         2. Lower Power Consumption
         3. Parallel Processing Capabilities
      3. Current Technological State
         1. Photonic Logic Gates
         2. Optical Interconnects
      4. Research Directions
         1. Integration with Existing Electronic Systems
         2. Scalability and Commercial Viability
         3. Overcoming Fabrication Hurdles
   4. Integration of AI-specific Instructions and Hardware
      1. AI-centric Processor Design
         1. Tensor Processing Units (TPUs)
         2. Graphics Processing Units (GPUs) for AI
         3. Neuromorphic Chips
      2. Instruction Set Enhancements
         1. Specialized Operations for Deep Learning
         2. Efficient Neural Network Execution
      3. Hardware Optimization for AI Workloads
         1. Memory Architecture Adjustments
         2. Enhanced Data Bandwidth
         3. Energy Consumption Reduction
      4. Deployment in Various Domains
         1. Autonomous Systems
         2. Natural Language Processing
         3. Real-time Data Analytics
      5. Future Challenges
         1. Developing Universal Standards
         2. Balancing General-Purpose and Specialized Hardware
         3. Keeping Pace with Algorithmic Advances