A basic level of competence with computer systems is a prerequisite for AI wizardry. If you are reading this, you probably already have a decent basic knowledge of computer architecture. As the scale of your AI efforts increase, it will become important to understand the mechanics of the machines performing your demands in order to deploy the maximum computer power possible and reduce the risks of failure.
![[DALLE3_ComputerArchitecture.png]]
## Concept Tree
### Basics
1. **Components of a Computer**: Understanding key components and their functions.
- CPU: Core processing unit
- Memory: RAM and ROM
- Storage: Hard Drives, SSDs
- Peripherals: Input/Output Devices
- Motherboard: Connecting All Components
2. **Data Representation**: Binary and number systems.
3. **Software vs Hardware**: Basic distinctions.
### More Advanced
1. **CPU Architecture & Instruction Cycle**:
- Fetch, Decode, Execute Cycle
- Registers and Buffers
- Pipelining
2. **Memory Hierarchy**:
- Cache Memory
- Virtual Memory
- Memory Management
3. **Operating Systems Basics**:
- Processes and Threads
- Scheduling
- File Systems
### Mastery
1. **Advanced CPU Architectures**:
- von Neumann Architecture
- Harvard Architecture
- Superscalar Architecture
2. **Performance Optimization & Parallel Processing**:
- Performance Metrics
- Multi-core Processing
- GPU Processing
3. **Impact of Architecture Design on Performance and Efficiency**:
- Power Consumption
- Scalability
- Reliability and Fault Tolerance