Computer Architecture – How Computers Work Inside

1. Introduction to Computer Architecture

Computer architecture is the science behind how computers are designed and how they work internally. It defines how a computer’s hardware and software interact to process information.

1.1 What Is Computer Architecture?

Definition:
Computer architecture refers to the design and organization of a computer’s essential components—such as the CPU, memory, input/output devices, and how they communicate.

Example:
Think of a computer as a factory. The architecture is the blueprint: it decides where each machine goes, how the materials flow from one process to the next, and what tools are used.

Key Aspects Include:

Instruction Set Architecture (ISA) – the set of instructions a CPU understands.
Microarchitecture – how the CPU executes those instructions (like pipelines, caches).
System Design – integration of CPU, memory, I/O, and peripherals.

1.2 Why Understanding Architecture Matters

Why it’s important:

Better Programming: Developers write better software when they understand how hardware executes code.
Performance Optimization: Knowing how memory and CPU interact helps optimize speed and efficiency.
Hardware Design: Engineers need it to build new processors and systems.
Troubleshooting: Helps in diagnosing system performance issues or failures.

Real-world Example:
Game developers often optimize for specific CPU or GPU architecture (like ARM or x86) to get smoother performance.

1.3 Evolution of Computer Systems

Computers didn’t always look or behave as they do today. Here’s a quick journey through their evolution:

Generation	Characteristics	Key Innovations
1st (1940s-50s)	Vacuum tubes, huge size	ENIAC, punch cards
2nd (1950s-60s)	Transistors, smaller & faster	IBM 1401
3rd (1960s-70s)	Integrated Circuits (ICs)	Mainframes, early PCs
4th (1970s-90s)	Microprocessors, GUIs	Personal computers, Intel 4004
5th (2000s–present)	Multi-core CPUs, AI chips	Smartphones, cloud, ML

Key Takeaway:
Computer architecture has evolved from bulky machines doing simple math to compact, powerful systems enabling AI and virtual reality.

1.4 Von Neumann vs Harvard Architectures

These are two fundamental types of computer design.

Von Neumann Architecture

Single memory for both data and instructions
Instructions are fetched and executed one at a time
Most common in general-purpose computers

Advantage: Simpler design
Disadvantage: Von Neumann bottleneck – only one thing (data or instruction) can be accessed at a time

Harvard Architecture

Separate memory for data and instructions
Can fetch data and instructions simultaneously

Advantage: Faster performance, especially in embedded systems
Disadvantage: More complex design

Analogy:
Von Neumann = One-lane road (shared for all traffic)
Harvard = Two-lane highway (separate lanes for data and instructions)

1.5 Modern Trends in Architecture (e.g., RISC-V, AI Chips)

Computer architecture continues to evolve to meet modern needs like speed, energy efficiency, and AI.

RISC-V (Reduced Instruction Set Computer – V)

Open-source CPU architecture
Designed for simplicity and modularity
Anyone can use or modify it—great for startups, research, and education
Competing with proprietary ones like ARM and x86

AI Chips (Accelerators like TPUs, NPUs)

Designed specifically for artificial intelligence tasks like deep learning
Faster than general CPUs for tasks like image recognition and language translation
Examples: Google’s TPUs, Apple’s Neural Engine, NVIDIA’s Tensor Cores

Other Trends:

3D chip stacking – more components in less space
Energy efficiency – for mobile and IoT devices
Quantum processors – future concept using qubits for parallel computation
Edge computing – smart chips in devices like cameras or drones to process data without cloud access

✅ Summary of Section 1:

Topic	Key Idea
1.1 What is Computer Architecture?	The internal design and structure of computer systems
1.2 Why It Matters	Helps improve programming, optimization, and innovation
1.3 Evolution	From vacuum tubes to AI-driven chips
1.4 Von Neumann vs Harvard	One shared memory vs separate memory for instructions and data
1.5 Modern Trends	Open architectures (RISC-V), AI-specific chips, efficiency and speed

2.2 Input Devices

Definition:
Input devices are tools that allow a user to communicate with the computer by providing data or commands.

Examples:

Keyboard – for typing text and commands
Mouse – for pointing and clicking
Scanner – converts physical documents to digital form
Microphone – captures sound
Camera – for images and video input
Touchscreen – combines input and output

Why Important:
They are the gateway for humans to feed raw data into a computer system.

2.3 Output Devices

Definition:
Output devices present information that the computer processes and converts into human-understandable form.

Examples:

Monitor/Display – shows visual output like text, videos, GUIs
Printer – provides a physical copy of documents or images
Speakers – produce sound output like music, speech
Projector – projects visual data onto a screen

Why Important:
Without output devices, we wouldn’t be able to see or hear the results of what a computer does.

2.4 System Bus (Address, Data, Control)

Definition:
A bus is a communication system that transfers data between components inside a computer.

There are 3 types of buses:

Type	Purpose
Data Bus	Carries the actual data being transferred
Address Bus	Carries information about where the data should go
Control Bus	Carries control signals (e.g., read/write commands)

Analogy:
Think of a computer as a city:

Data bus = vehicles carrying goods (data)
Address bus = GPS guiding where the goods go
Control bus = traffic lights/rules controlling movement

Importance:
Without the system bus, the CPU wouldn’t be able to talk to memory, storage, or I/O devices.

2.5 Storage Hierarchy Overview

Definition:
The storage hierarchy shows the different levels of memory in a system, ranked by speed, size, and cost.

Pyramid of Storage:

cssCopyEdit       Registers (Fastest, Smallest)
          ↓
       Cache (L1, L2, L3)
          ↓
       RAM (Main Memory)
          ↓
       SSD/HDD (Secondary Storage)
          ↓
       Cloud/External Drives (Slowest, Largest)

Level	Speed	Cost	Size
Registers	Extremely fast	Very high	Very small
Cache	Very fast	High	Small
RAM	Fast	Medium	Moderate
HDD/SSD	Slower	Low	Large
Cloud/External	Slowest	Varies	Unlimited (virtually)

Key Idea:

Fast memory is expensive and small.
Slower memory is cheaper and larger.
The CPU uses the top layers frequently, and moves data up and down this hierarchy as needed.

✅ Summary of Section 2:

Subtopic	Key Idea
2.1 Overview	Computer has five key parts working together
2.2 Input Devices	Tools like keyboards and mice used to feed data into the system
2.3 Output Devices	Devices like monitors and printers show results
2.4 System Bus	Connects components through data, address, and control lines
2.5 Storage Hierarchy	Organizes memory from fast/expensive to slow/large

3. Central Processing Unit (CPU)

The CPU is the “brain of the computer.” It performs calculations, makes decisions, and controls the flow of data. Every instruction that runs on a computer passes through the CPU.

3.1 Anatomy of a CPU

A CPU is made up of several essential internal parts that work together to process instructions.

Main Components:

Control Unit (CU) – directs operations and manages instruction flow
Arithmetic Logic Unit (ALU) – performs all calculations and logical operations
Registers – tiny, fast memory slots inside the CPU
Cache Memory – stores frequently used data for quick access
Clock – synchronizes the CPU’s operations (measured in GHz)

Analogy:
Think of the CPU as a factory:

The ALU is the worker doing the actual tasks.
The CU is the manager telling the worker what to do and when.
The Registers are sticky notes on the worker’s desk (quick access).
The Cache is like a small shelf with tools often used.

3.2 Control Unit (CU)

Function:
The Control Unit directs all operations inside the computer. It does not process data, but it:

Decodes instructions
Sends control signals to other parts of the CPU and memory
Manages the flow of data between the CPU and other components

Key Role:

Tells the ALU what operation to perform
Coordinates movement between memory, I/O, and CPU

Simple View:
The CU is like a traffic controller, managing data flow and ensuring everything happens in the correct order.

3.3 Arithmetic Logic Unit (ALU)

Function:
The ALU performs arithmetic and logical operations.

Type of Operation	Examples
Arithmetic	Addition, subtraction, multiplication, division
Logical	AND, OR, NOT, comparisons (>, <, =)

Real-World Example:
If you’re calculating 2 + 2, the ALU handles the math.
If you’re checking “is 5 > 3?”, the ALU does the comparison.

ALU + CU = Core Function of CPU

3.4 Registers and Their Types

Definition:
Registers are very small, high-speed memory locations inside the CPU that hold data and instructions temporarily during processing.

Key Types:

Accumulator (ACC) – stores intermediate arithmetic/logic results
Program Counter (PC) – keeps track of the next instruction’s address
Instruction Register (IR) – holds the current instruction being executed
Memory Address Register (MAR) – holds the address of memory to be accessed
Memory Data Register (MDR) – holds data being transferred to/from memory

Importance:
Registers are faster than RAM, enabling the CPU to access and store temporary data almost instantly.

3.5 Instruction Cycle: Fetch, Decode, Execute

This is how the CPU processes every instruction:

1. Fetch

The CPU gets (fetches) the instruction from memory (RAM)
Uses the Program Counter to know where the instruction is

2. Decode

The Control Unit decodes the instruction to understand what needs to be done

3. Execute

The ALU or another CPU part performs the task (e.g., adding numbers)

Cycle Repeats

After execution, the Program Counter moves to the next instruction

Example:
Instruction: ADD A, B

Fetch: Get the command
Decode: Understand it’s an addition
Execute: ALU adds values from register A and B

3.6 Clock Speed and Performance

Definition:
Clock speed is the rate at which a CPU can execute instructions, measured in GHz (Gigahertz).

Clock Speed	Approximate Meaning
1 GHz	1 billion cycles per second
3.5 GHz	3.5 billion cycles per second

BUT clock speed isn’t everything. Other factors include:

Number of cores (more tasks in parallel)
Cache size
Instruction efficiency (RISC vs CISC)
Pipeline and execution design

Performance Factors:

Cores: Modern CPUs have multiple cores (2, 4, 8, 16+)
Threads: Some CPUs handle two threads per core (hyperthreading)
Architecture: Efficient design can outperform a faster clock

Analogy:
Clock speed = speed of a single car
Cores = number of cars on the road
Cache = how close the fuel station is

✅ Summary of Section 3:

Subtopic	Key Point
3.1 Anatomy	CPU has CU, ALU, Registers, Cache, Clock
3.2 Control Unit	Manages and coordinates instructions
3.3 ALU	Performs calculations and logic
3.4 Registers	Super-fast internal memory slots
3.5 Instruction Cycle	Fetch → Decode → Execute
3.6 Clock Speed	Measures instruction rate, affects performance

3. Central Processing Unit (CPU)

The CPU (Central Processing Unit) is the brain of the computer. It handles all instructions it receives from hardware and software, performing calculations, logical decisions, and managing data flow.

3.1 Anatomy of a CPU

The CPU has several core components working together to process data efficiently:

Key Parts:

Control Unit (CU) – Directs operations of the processor.
Arithmetic Logic Unit (ALU) – Performs arithmetic and logical calculations.
Registers – Temporary storage inside the CPU for fast data access.
Cache Memory – Stores frequently accessed data for quick use.
Internal Clock – Synchronizes operations (measured in GHz).

Simple Analogy:

Imagine a kitchen:

ALU is the chef doing the cooking.
CU is the head chef giving instructions.
Registers are small bowls with ingredients ready.
Cache is a nearby pantry.
Clock is a timer ensuring things are done in rhythm.

3.2 Control Unit (CU)

The Control Unit acts as the traffic controller of the CPU.

Functions:

Fetches instructions from memory.
Decodes them to understand what action is needed.
Sends control signals to coordinate with the ALU, memory, and I/O devices.

Example:

If you tell the computer to add two numbers:

The CU finds the addition instruction in memory.
It sends the numbers to the ALU.
It tells the ALU to perform the addition.

The CU doesn’t perform calculations—it coordinates everything.

3.3 Arithmetic Logic Unit (ALU)

The ALU is where the actual processing happens.

Responsibilities:

Arithmetic operations: addition, subtraction, multiplication, division.
Logical operations: AND, OR, NOT, comparisons (greater than, less than).

Real-world Analogy:

Like a calculator inside your computer, the ALU processes numbers and makes decisions.

3.4 Registers and Their Types

Registers are very small, very fast memory units located inside the CPU. They hold data that the CPU needs immediately.

Key Types of Registers:

Register	Purpose
Accumulator (ACC)	Holds results of calculations
Program Counter (PC)	Tracks the address of the next instruction
Instruction Register (IR)	Holds the current instruction being executed
Memory Address Register (MAR)	Holds the address from/to memory
Memory Data Register (MDR)	Temporarily holds data going to/from memory

Why They’re Important:

Registers make the CPU work faster by avoiding delays of fetching from RAM.

3.5 Instruction Cycle: Fetch, Decode, Execute

Every task your computer performs follows this basic cycle:

Step 1: Fetch

The Program Counter (PC) gives the address of the next instruction.
The CPU fetches the instruction from RAM and stores it in the Instruction Register (IR).

Step 2: Decode

The Control Unit decodes the instruction to determine what needs to be done.

Step 3: Execute

The ALU performs the task (e.g., a calculation or data movement).
The result may be stored in a register or sent to memory.

Then the cycle repeats…

This cycle happens billions of times every second.

3.6 Clock Speed and Performance

The clock is like a metronome—it sets the pace for how fast the CPU works.

Measured in:

Hertz (Hz) – cycles per second.
1 GHz = 1 billion instructions per second.

But performance isn’t only about speed:

Factor	Role
Clock Speed	Determines how fast instructions are executed
Cores	More cores = more parallel processing
Cache Size	Larger cache = faster access to common data
Instruction Set Architecture	More efficient instruction sets do more in less time
Thermal Management	Heat can limit performance—cooling helps CPUs run better

Example:

A 2.5 GHz quad-core CPU can execute tasks more efficiently than a 3.0 GHz single-core if the software uses all cores well.

✅ Summary of Section 3

Topic	Summary
3.1 Anatomy of CPU	CPU includes CU, ALU, Registers, Cache, and Clock
3.2 Control Unit (CU)	Manages instruction flow and coordinates all operations
3.3 ALU	Handles math and logic operations
3.4 Registers	Super-fast memory used for current operations
3.5 Instruction Cycle	Fetch → Decode → Execute – core cycle of CPU
3.6 Clock Speed	Determines how fast a

4. Memory and Storage Systems

A computer needs memory to temporarily store data it’s working with, and storage to save data permanently. These systems together determine how efficiently a computer can access and retain information.

4.1 RAM vs ROM

RAM (Random Access Memory)

Volatile memory – data is lost when the computer turns off.
Used to store data and programs that the CPU is actively using.
Fast and temporary.
Example: When you open a game or a browser, it loads into RAM.

ROM (Read-Only Memory)

Non-volatile memory – retains data even when the computer is off.
Contains firmware – permanent instructions like the BIOS (basic startup system).
You can’t normally write to ROM during operation.

Feature	RAM	ROM
Volatile	Yes	No
Writable	Yes	No (usually)
Speed	High	Lower
Use	Temporary storage	Permanent startup instructions

4.2 Cache Memory: L1, L2, L3

Cache is a small, super-fast memory located inside or very close to the CPU.

Purpose:

Stores frequently accessed instructions and data to speed up processing.
Reduces time spent accessing data from RAM.

Levels:

Level	Location	Speed	Size
L1	Inside CPU core	Fastest	Smallest (KBs)
L2	Near core	Very fast	Larger (MBs)
L3	Shared across cores	Fast	Largest (up to tens of MBs)

Analogy:
L1 is like a chef’s pocket, L2 is the kitchen counter, L3 is the nearby storage room, and RAM is the supermarket down the street.

4.3 Virtual Memory and Paging

Sometimes, a computer runs more programs than can fit in RAM. That’s where virtual memory comes in.

Virtual Memory:

Uses part of the hard drive (HDD or SSD) to act like RAM.
Slower than real RAM, but helps prevent crashes.

Paging:

Splits memory into small blocks called pages.
The Operating System swaps pages between RAM and virtual memory as needed.
If RAM is full, less-used pages are moved to disk (page file).

Problem:
Too much paging = slower performance (called thrashing).

4.4 Secondary Storage: HDDs and SSDs

This is the computer’s long-term storage—it holds your files, software, and operating system.

HDD (Hard Disk Drive):

Uses spinning magnetic disks to store data.
Cheaper, more storage space.
Slower than SSDs.

SSD (Solid State Drive):

Uses flash memory (no moving parts).
Faster, more durable, more expensive per GB.

Feature	HDD	SSD
Speed	Slower	Faster
Cost	Cheaper	More expensive
Durability	Less	More
Noise	Audible	Silent

Example:
Installing your operating system on an SSD makes your computer boot up much faster.

4.5 Flash Memory and Cloud Storage

Flash Memory:

Non-volatile, electronic memory with no moving parts.
Used in USB drives, SD cards, and SSDs.
Faster than HDDs, portable, and reliable.

Cloud Storage:

Data stored on remote servers accessed via the internet.
Examples: Google Drive, Dropbox, OneDrive
Enables access from anywhere and acts as a backup solution.

Type	Used in
Flash Memory	USB drives, SSDs, smartphones
Cloud Storage	Web apps, backups, collaboration tools

4.6 Memory Management Unit (MMU)

The MMU is a part of the CPU that handles memory access.

Key Functions:

Translates virtual addresses (used by programs) into physical addresses (in actual RAM).
Manages paging, segmentation, and protection.
Prevents one program from accessing another program’s memory (important for security).

Example:
If two programs are open at the same time, the MMU ensures they don’t interfere with each other’s data.

Why It’s Important:

Without the MMU, systems would crash or be vulnerable to attacks like buffer overflows.

✅ Summary of Section 4:

Subtopic	Key Idea
4.1 RAM vs ROM	RAM is temporary and fast; ROM is permanent and holds startup code
4.2 Cache	Very fast memory close to the CPU (L1, L2, L3)
4.3 Virtual Memory	Extends RAM using disk storage; paging manages memory swapping
4.4 HDDs and SSDs	Secondary storage; SSDs are faster and more durable
4.5 Flash & Cloud	Flash is fast local storage; cloud stores data online
4.6 MMU	Manages memory addresses, security, and efficient usage

5. Instruction Set Architecture (ISA)

ISA is the language of the CPU. It defines how software tells the hardware what to do. It acts as the bridge between programs and the physical computer.

5.1 What Is an ISA?

Definition:
An Instruction Set Architecture (ISA) is the set of basic instructions a CPU can understand and execute. It specifies:

The instructions (like ADD, SUB, LOAD)
Registers
Data types
Memory access methods
Instruction formats

Why It Matters:

Software must be written in a way the CPU understands.
Each type of CPU (Intel, ARM, etc.) has its own ISA.

Analogy:
Think of the ISA as a language manual. If your CPU speaks “x86,” it only understands that specific instruction set.

5.2 RISC vs CISC

RISC (Reduced Instruction Set Computer)

Fewer, simpler instructions
Each instruction executes in one clock cycle
Faster and more efficient
Requires more lines of code to do complex tasks

Used In: ARM, RISC-V, MIPS

CISC (Complex Instruction Set Computer)

Many complex instructions
One instruction may take multiple cycles
Easier for programmers (fewer lines of code)
Hardware is more complex

Used In: Intel x86, AMD processors

Feature	RISC	CISC
Instruction count	Fewer	More
Instruction complexity	Simple	Complex
Hardware	Simpler	More complex
Example CPUs	ARM, RISC-V	Intel x86

5.3 Common ISAs: x86, ARM, MIPS, RISC-V

x86

Dominant in PCs and laptops
CISC-based
Developed by Intel
Powerful but energy-hungry

ARM

Widely used in smartphones, tablets, and IoT devices
RISC-based
Very energy efficient
Used by Apple (M1, M2 chips) and most Android phones

MIPS

RISC-based, used in education and some embedded systems
Simple design, great for learning architecture

RISC-V

Open-source RISC ISA
Free to use, modify, and extend
Gaining popularity in research, startups, and academia

5.4 Machine Language vs Assembly Language

Machine Language

Binary code (0s and 1s)
Directly executed by the CPU
Hard to read and write for humans

Example:
10110000 01100001

Assembly Language

Human-readable representation of machine language
Uses mnemonics (short codes like MOV, ADD, SUB)
Must be translated into machine code by an assembler

Example:

asmCopyEditMOV AL, 61h  ; Move hexadecimal 61 into register AL

Language	Human-readable	CPU-executable	Requires translation
Machine	No	Yes	No
Assembly	Yes	No	Yes (via assembler)

5.5 Addressing Modes

Definition:
Addressing modes define how operands (data) are accessed in instructions.

Common Modes:

Mode	Description	Example
Immediate	Data is part of the instruction	`MOV A, #5`
Register	Operand is in a register	`ADD A, B`
Direct	Data is in a specific memory address	`MOV A, [1000]`
Indirect	Memory address is stored in a register	`MOV A, [BX]`
Indexed	Combines base address with offset	`MOV A, [BX + SI]`

Why Important?
Different modes allow flexible ways to access and manipulate data efficiently.

5.6 Micro-operations and Microinstructions

Micro-operations:

Low-level operations performed within the CPU.
Include things like transferring data between registers or performing an ALU task.

Example:
Instruction ADD A, B may involve these micro-operations:

Load A into temporary register
Load B into ALU
Perform addition
Store result back in A

Microinstructions:

Control-level commands that trigger micro-operations.
Generated by the control unit, especially in microprogrammed control.

Analogy:
Think of a full instruction (like ADD) as a recipe.
Micro-operations are the steps in the recipe (crack eggs, mix, cook).

✅ Summary of Section 5:

Subtopic	Key Point
5.1 What is ISA?	It’s the CPU’s language – defines how instructions are understood and executed
5.2 RISC vs CISC	RISC = simple & fast; CISC = complex but fewer instructions
5.3 Common ISAs	x86 (PCs), ARM (phones), MIPS (learning), RISC-V (open-source future)
5.4 Machine vs Assembly	Machine = binary; Assembly = readable format for programmers
5.5 Addressing Modes	Ways to access data in instructions
5.6 Micro-operations	Internal steps that the CPU takes to execute instructions

6. Data Path and Control Path

The data path and control path are the two main internal parts of the CPU that work together to execute instructions.

Data Path: Handles the actual movement and processing of data.
Control Path: Generates the signals that guide the data path on what to do.

Think of the CPU as a kitchen:

The data path is like the chefs and cooking equipment.
The control path is like the recipe instructions telling the chefs what steps to take.

6.1 Data Path Elements

These are the physical components that process and move data within the CPU.

Key Elements:

Registers: Small memory locations for storing intermediate data (like variables in math).
ALU (Arithmetic Logic Unit): Performs math and logic operations.
Multiplexers (MUXes): Choose between data sources (like a switch).
Memory Units: Access memory to read/write data.
Buses: Channels that move data from one part to another.

Example: To perform A = B + C, the data path:

Loads B and C from registers
Sends them to the ALU
ALU adds them
Result is stored in register A

6.2 Control Signals and Logic

The control unit generates signals that tell each data path component what to do at every clock cycle.

Control Signals Examples:

RegWrite: Enable writing into a register
MemRead: Read from memory
ALUOp: Tell ALU what operation to perform (add, subtract, etc.)
PCWrite: Update the program counter

Types of Control Logic:

Combinational Logic: Output depends only on current inputs
Sequential Logic: Output depends on current inputs + past states (via memory/flip-flops)

Analogy: The control signals are like buttons on a remote that control which appliance does what and when.

6.3 Hardwired Control vs Microprogrammed Control

There are two main ways to implement the control unit:

Hardwired Control

Uses fixed logic circuits (gates, flip-flops)
Fast but inflexible
Changes require rewiring hardware

Used In: High-speed systems like gaming CPUs

Microprogrammed Control

Uses small software-like programs (microinstructions)
Flexible and easier to update
Slightly slower

Used In: General-purpose CPUs like Intel and AMD

Feature	Hardwired	Microprogrammed
Speed	Faster	Slower
Flexibility	Low	High
Complexity	High	Easier to design
Example	RISC processors	CISC processors

6.4 Pipelining Concepts

Pipelining is like an assembly line in a factory. It allows the CPU to work on multiple instructions at the same time, but in different stages.

Basic Stages:

Fetch: Get the instruction from memory
Decode: Understand what to do
Execute: Perform the action
Memory Access: Read/write data from memory
Write-back: Store the result

Benefit: Improves performance by increasing instruction throughput.

Analogy: Like a car wash where multiple cars are in different wash stages simultaneously.

6.5 Hazards: Data, Control, and Structural

Hazards are problems that stop the pipeline from working smoothly.

1. Data Hazard

When one instruction needs the result of another that hasn’t finished yet.
Example: ADD R1, R2, R3 followed by SUB R4, R1, R5

2. Control Hazard

Caused by branching/jumping (e.g., if-else)
CPU doesn’t know which instruction to fetch next.

3. Structural Hazard

When two instructions need the same hardware at the same time (e.g., both want the ALU)

Solution Methods:

Stalling (pause the pipeline)
Forwarding (pass result directly)
Branch prediction (guess direction of branches)

6.6 Branch Prediction and Speculative Execution

Branch Prediction

CPU guesses the outcome of a conditional instruction to keep the pipeline full.
If guessed right → faster performance.
If guessed wrong → must discard wrong results (called pipeline flush).

Speculative Execution

CPU executes instructions ahead of time before knowing if they’re needed.
Speeds things up but must be canceled if branch prediction fails.

Used Heavily In: Modern high-performance CPUs (e.g., Intel i9, Apple M-series)

Security Note: Speculative execution was exploited in famous vulnerabilities like Spectre and Meltdown.

✅ Summary of Section 6:

Subtopic	Key Idea
6.1 Data Path Elements	Actual hardware that moves and processes data (ALU, registers, etc.)
6.2 Control Logic	Signals that direct data path behavior
6.3 Control Types	Hardwired (fast) vs Microprogrammed (flexible)
6.4 Pipelining	Overlapping instruction execution to speed up processing
6.5 Hazards	Pipeline interruptions due to dependencies or conflicts
6.6 Branch Prediction	Predict and pre-execute instructions to avoid delays

7. Performance and Optimization

Understanding how to measure and improve computer performance is crucial for designing fast and efficient systems. This section explores how performance is evaluated, tested, and optimized through various techniques.

7.1 Measuring Performance: MIPS, FLOPS, CPI

MIPS (Million Instructions Per Second)

Tells how many instructions a CPU can execute per second.
Simple, but not always accurate, since instructions vary in complexity.
Good for rough comparison, especially within the same family of CPUs.

FLOPS (Floating Point Operations Per Second)

Measures floating-point computation speed (used in scientific or graphics tasks).
Important for supercomputers, AI models, 3D rendering, and simulations.
Example: 1 TFLOPS = 1 trillion floating-point operations/second.

CPI (Cycles Per Instruction)

Measures average number of clock cycles needed per instruction.
Lower CPI = better efficiency.
Formula: CPU Time=Instruction Count×CPI×Clock Cycle Time\text{CPU Time} = \text{Instruction Count} \times \text{CPI} \times \text{Clock Cycle Time}CPU Time=Instruction Count×CPI×Clock Cycle Time

Summary Table:

Metric	What it Measures	Good For
MIPS	Instruction throughput	Basic CPU performance
FLOPS	Floating-point power	Scientific/AI tasks
CPI	Instruction efficiency	Architecture optimization

7.2 Benchmarks and Testing

Benchmarks

Standard programs/tests used to compare performance of different systems.
Examples:
- SPEC (Standard Performance Evaluation Corporation) for general CPUs.
- Geekbench for phones and desktops.
- 3DMark for gaming/graphics systems.

Types of Testing

Synthetic Benchmarks: Focused, artificial tests (e.g., memory, CPU, GPU).
Real-World Benchmarks: Run actual software workloads (e.g., rendering a video, running a game).

Why Important?
Benchmarking helps:

Compare CPUs and GPUs
Identify bottlenecks
Decide if an upgrade is worth it

7.3 Overclocking and Thermal Constraints

Overclocking

Running a CPU/GPU at higher speed than rated.
Increases performance but generates more heat and power consumption.
Must be done carefully to avoid system instability or damage.

Thermal Constraints

CPUs generate heat when running; overheating can damage them.
Thermal Throttling: CPU slows itself down to avoid overheating.
Cooling Solutions:
- Air cooling (fans, heatsinks)
- Liquid cooling
- Thermal paste for better contact

Balance: More speed ↔ more heat → need better cooling

7.4 Multicore and Parallelism

Multicore Processors

Modern CPUs have multiple cores (e.g., dual-core, quad-core, octa-core).
Each core can run independent tasks simultaneously.
Improves performance in multitasking and multithreaded applications.

Parallelism

Dividing tasks across multiple cores or processors.
Used in servers, scientific computing, and gaming.

Example:
While one core handles video playback, another can run background updates.

7.5 Instruction-Level Parallelism (ILP)

ILP means the CPU tries to execute multiple instructions at once, even within a single core.

Techniques:

Pipelining: Overlaps instruction stages.
Superscalar Execution: Uses multiple execution units to run instructions in parallel.
Out-of-Order Execution: Executes instructions not in program order, if dependencies allow.
Register Renaming: Avoids conflicts between instructions using the same registers.

Goal: Increase CPU efficiency without waiting on one instruction to finish before starting the next.

7.6 Hardware Acceleration (e.g., GPUs, TPUs)

Sometimes, CPUs alone aren’t fast enough for certain tasks, so we use specialized hardware.

GPU (Graphics Processing Unit)

Originally for graphics, now used in AI, video editing, gaming.
Has thousands of cores, great for parallel processing.

TPU (Tensor Processing Unit)

Developed by Google, optimized for AI and machine learning.
Faster and more efficient than GPUs for deep learning models.

Other Accelerators:

FPGAs (Field-Programmable Gate Arrays): Reprogrammable chips for custom logic.
ASICs (Application-Specific Integrated Circuits): Custom-made chips for specific tasks (e.g., Bitcoin mining).

Why Use Them?

Free up CPU resources
Speed up specific tasks
Save energy in repeated operations

✅ Summary of Section 7

Topic	Key Takeaway
7.1 Measuring Performance	Use MIPS, FLOPS, and CPI to quantify CPU speed and efficiency
7.2 Benchmarks	Standard tests that show real or synthetic performance
7.3 Overclocking & Heat	Boost performance, but watch for thermal limits
7.4 Multicore CPUs	Multiple cores = better multitasking and parallel work
7.5 Instruction-Level Parallelism	Smart internal CPU tricks to run instructions faster
7.6 Hardware Accelerators	GPUs, TPUs, and ASICs boost performance in specific tasks

8. Input/Output Systems

Input/Output (I/O) systems connect the CPU and memory with external devices, enabling communication between the computer and the outside world. This section explores how I/O works, the technologies involved, and how performance is optimized.

8.1 I/O Devices Overview

Input Devices

Devices that send data to the computer.
Examples: Keyboard, mouse, touchscreen, scanner, microphone, webcam.

Output Devices

Devices that receive data from the computer and present it to the user.
Examples: Monitor, printer, speakers, projector, VR headset.

Input/Output Devices (Both)

Some devices can perform both functions.
Examples: Touchscreen (input + output), external hard drives, network cards.

I/O Roles

I/O devices are slower than CPU, so the system needs mechanisms (like buffers, interrupts) to handle this speed difference efficiently.

8.2 I/O Bus and Interfaces

System Bus Recap

A bus is a communication pathway connecting components.
Three types: Data Bus, Address Bus, Control Bus.

I/O Bus

Special bus that connects I/O devices to the CPU/memory system.
Examples of I/O buses:
- USB for external peripherals
- PCIe for internal high-speed devices like GPUs
- SATA for storage

I/O Interface

Each I/O device needs an interface controller to:
- Translate CPU instructions to device signals
- Manage communication protocols
- Buffer data transfers

8.3 Interrupts and DMA (Direct Memory Access)

Interrupts

When an I/O device needs attention, it sends an interrupt signal to the CPU.
CPU pauses current task, handles the device, then resumes.
Efficient because the CPU doesn’t have to check the device constantly.

DMA (Direct Memory Access)

Allows a device to transfer data directly to/from memory without CPU help.
Frees up CPU for other tasks.
Example: While copying a file to a USB drive, CPU isn’t fully occupied—DMA manages the transfer.

Without DMA: CPU reads → stores → writes → repeats
With DMA: Device ↔ Memory (CPU just initiates and monitors)

8.4 Polling vs Interrupt-Driven I/O

Feature	Polling	Interrupt-Driven I/O
Method	CPU checks device repeatedly	Device notifies CPU via interrupt
CPU Efficiency	Wastes time checking	Efficient, responds only when needed
Usage	Simple, low-speed devices	Complex or high-speed devices
Example	Checking keyboard buffer	Mouse click, disk transfer complete

Polling is easier to implement but inefficient. Interrupts are more powerful for multitasking and real-time systems.

8.5 USB, SATA, PCIe, and Thunderbolt Interfaces

USB (Universal Serial Bus)

Common interface for keyboards, mice, storage devices.
Versions: USB 2.0 (slow), USB 3.0/3.1/3.2 (fast), USB-C (reversible, high speed).
Supports hot-swapping and plug-and-play.

SATA (Serial ATA)

Used for internal storage devices like HDDs and SSDs.
Provides faster data transfer than older PATA.
Hot-swappable in most modern systems.

PCIe (Peripheral Component Interconnect Express)

High-speed interface for internal devices like:
- Graphics cards
- Network cards
- NVMe SSDs
Offers different lanes (x1, x4, x8, x16) for varying bandwidth.

Thunderbolt

High-speed interface developed by Intel and Apple.
Combines PCIe + DisplayPort + Power.
Used for external GPUs, docks, and displays.
Thunderbolt 3 and 4 use USB-C connectors.

8.6 Role of Device Drivers

What Are Device Drivers?

Software components that allow the OS to communicate with hardware.
Translate generic OS instructions into specific hardware commands.

Functions of a Driver

Identify and configure the device.
Send and receive data.
Handle interrupts or errors.
Update firmware or settings.

Driver Examples

Printer driver: Translates print commands into printer-understandable data.
GPU driver: Optimizes rendering and performance on your system.

Without proper drivers, even the best hardware won’t function correctly.

✅ Summary of Section 8

Topic	Key Point
I/O Devices	Enable user-computer interaction through input and output
I/O Buses	Connect devices with CPU/memory using standard protocols
Interrupts & DMA	Improve system efficiency by offloading or signaling the CPU
Polling vs Interrupts	Trade-off between simplicity and CPU usage
Interfaces (USB, SATA, etc.)	Different technologies for connecting peripherals
Device Drivers	Essential software bridges between hardware and OS

9. Storage Architecture

Storage architecture refers to the organization, management, and technology behind how data is stored, accessed, and protected in a computer system.

9.1 File System Interaction with Hardware

File System is the software layer that organizes files and directories on storage devices.
Common file systems: NTFS (Windows), ext4 (Linux), HFS+ / APFS (Mac).
The file system translates user-friendly file operations (open, save, delete) into hardware-level commands to read/write sectors or blocks on disks.
It manages:
- Allocation of space for files.
- Metadata such as file size, permissions, timestamps.
- Error checking and recovery.

Example: When you save a document, the file system decides where on the disk it goes, and tells the hardware how to write it.

9.2 RAID and Data Redundancy

RAID (Redundant Array of Independent Disks) is a technique combining multiple physical disks into one logical unit for:

Performance improvement
Data redundancy (protection against disk failure)

Common RAID Levels:

RAID Level	Description	Benefits	Drawbacks
RAID 0	Data striping (split across disks)	Faster read/write	No redundancy, data lost if one disk fails
RAID 1	Mirroring (duplicate data on two disks)	Data protection	Uses double storage capacity
RAID 5	Striping with parity (error correction info)	Good balance of speed & safety	Needs at least 3 disks; slower writes
RAID 6	Like RAID 5 but with double parity	Can tolerate two disk failures	More overhead

9.3 Access Times and Performance Metrics

Access time is the delay before data transfer begins, important in storage speed.

Seek Time: Time for disk head to move to the correct track (important for HDD).
Rotational Latency: Wait time for disk sector to rotate under head (HDD).
Transfer Rate: Speed of reading/writing data once positioned.
IOPS (Input/Output Operations Per Second): Number of operations a device can handle per second.

HDD vs SSD:

HDDs have higher seek time & latency due to moving parts.
SSDs have almost zero seek time and very fast transfer rates.

9.4 Emerging Storage: NVMe, Optane, 3D NAND

NVMe (Non-Volatile Memory Express)

Protocol designed for fast SSDs connected via PCIe.
Reduces latency and increases throughput compared to SATA SSDs.
Used in high-performance laptops and servers.

Intel Optane

Combines 3D XPoint memory technology for extremely fast access.
Used as a cache between RAM and storage or as storage itself.
Faster than traditional NAND flash, closer to RAM speeds.

3D NAND

Flash memory stacked vertically in layers.
Increases storage density and reduces cost.
Most modern SSDs use 3D NAND for higher capacity.

9.5 Storage Virtualization and Tiered Storage

Storage Virtualization

Abstracts physical storage devices into a single logical pool.
Improves management, flexibility, and scalability.
Common in cloud environments and enterprise storage systems.

Tiered Storage

Data is stored on different types of storage based on importance and access frequency.
Hot data (frequently accessed) stored on fast SSDs.
Cold data (rarely accessed) moved to slower, cheaper HDDs or cloud.
Optimizes cost and performance.

✅ Summary of Section 9:

Subtopic	Key Idea
9.1 File System & Hardware	File systems manage how data is stored and accessed on physical devices
9.2 RAID	Combines disks for speed and/or redundancy
9.3 Access Times	Measures like seek time and IOPS determine storage speed
9.4 Emerging Tech	NVMe, Optane, 3D NAND improve speed and density
9.5 Virtualization & Tiering	Abstract storage and optimize data placement for cost & speed

10. Parallel and Distributed Architectures

As computing demands grow, architectures evolve to handle more work simultaneously and across multiple machines. This section explores the concepts behind these systems.

10.1 SMP vs MPP vs NUMA

SMP (Symmetric Multiprocessing)

Multiple identical processors share the same memory.
Processors are peers, can access all memory equally.
Used in many multi-core desktop and server systems.

MPP (Massively Parallel Processing)

Many processors with their own private memory.
Connected by a high-speed network.
Used in supercomputers and large-scale data centers.
Good for tasks that can be split into independent parts.

NUMA (Non-Uniform Memory Access)

Processors have local memory that they access faster.
Also can access other processors’ memory but slower.
Balances SMP’s shared memory ease with MPP’s scalability.

Architecture	Memory Model	Use Case
SMP	Shared, uniform access	Multi-core PCs/servers
MPP	Distributed, private memory	Supercomputing, big data
NUMA	Shared but non-uniform access	High-performance servers

10.2 Multithreading and Hyperthreading

Multithreading

CPU runs multiple threads (smaller tasks) of a program concurrently.
Improves utilization of CPU resources.
Found in many modern CPUs.

Hyperthreading (Intel’s Trademark)

A form of multithreading that lets a single CPU core appear as two logical cores.
Allows simultaneous execution of two threads per core.
Improves performance when threads share CPU resources efficiently.

10.3 Cluster Computing

A cluster is a group of independent computers (nodes) working together.
Nodes communicate via a network.
Clusters provide high availability, scalability, and power for large tasks.
Used in scientific research, web services, and databases.

10.4 Grid and Cloud Architectures

Grid Computing

Combines computing resources from multiple locations into a virtual supercomputer.
Focuses on collaborative resource sharing.
Used for large-scale scientific problems.

Cloud Computing

Provides on-demand access to computing resources over the internet.
Users pay for usage without owning hardware.
Offers scalability, flexibility, and managed services.
Examples: AWS, Azure, Google Cloud.

10.5 GPU vs CPU Architecture

Feature	CPU	GPU
Cores	Few (4-32)	Thousands
Purpose	General-purpose tasks	Parallel tasks, graphics, AI
Control	Complex control logic	Simple, repetitive operations
Memory	Large caches, complex hierarchy	High memory bandwidth, smaller caches

GPUs excel at parallel processing of similar tasks (e.g., image rendering), while CPUs handle diverse, sequential tasks.

10.6 Quantum and Neuromorphic Computing (Intro)

Quantum Computing

Uses quantum bits (qubits) which can represent 0, 1, or both simultaneously.
Can solve certain problems exponentially faster than classical computers.
Still experimental, but promising for cryptography, optimization.

Neuromorphic Computing

Mimics the structure of the human brain.
Uses networks of artificial neurons and synapses.
Designed for tasks like pattern recognition and sensory processing.
Still in research stages but could revolutionize AI.

✅ Summary of Section 10

Topic	Key Point
SMP, MPP, NUMA	Different models of multiprocessing and memory access
Multithreading & Hyperthreading	Running multiple threads per core for efficiency
Cluster Computing	Multiple computers working as one system
Grid & Cloud	Distributed computing models with shared or rented resources
GPU vs CPU	GPUs specialize in parallel tasks; CPUs are versatile
Quantum & Neuromorphic	Emerging computing paradigms based on quantum physics and brain models

11. Power, Heat, and Energy Efficiency

Modern computers must balance performance with power consumption and heat dissipation to stay efficient, especially in mobile and data center environments.

11.1 Power Consumption in CPUs

CPUs consume power mainly when switching transistors during computation.
Higher clock speeds and more cores increase power use.
Power consumption impacts battery life in mobiles and electricity cost in data centers.
Static power: Power used even when idle (leakage currents).
Dynamic power: Power used during active switching.

11.2 Cooling Techniques (Air, Liquid, Thermoelectric)

Air Cooling

Most common and cheapest.
Uses fans and heat sinks to move heat away.
Efficient for everyday PCs.

Liquid Cooling

Circulates liquid coolant through tubes and radiators.
More effective at removing heat, quieter operation.
Used in gaming PCs and servers.

Thermoelectric Cooling

Uses Peltier devices to move heat via electricity.
Can cool below ambient temperature.
More expensive and less common.

11.3 Dynamic Voltage and Frequency Scaling (DVFS)

Technique to adjust CPU voltage and clock speed on the fly.
Reduces power consumption during low workload.
Balances performance and energy efficiency.
Used in smartphones and laptops to save battery.

11.4 Energy-Efficient Architectures

Designs that minimize power use without sacrificing performance.
Examples include:
- ARM processors for mobile devices.
- Use of low-power cores in big.LITTLE architectures.
- Specialized cores for specific tasks (e.g., AI accelerators).

11.5 Mobile Processor Design Considerations

Must prioritize low power and heat dissipation.
Use of DVFS and energy-efficient cores.
Integration of components to reduce power loss.
Smaller fabrication nodes (e.g., 5nm technology) improve efficiency.

12. Security in Hardware Architecture

Hardware is the foundation for computer security, protecting systems from threats starting at the physical level.

12.1 Hardware-Level Security Features

Trusted Platform Modules (TPM): Secure cryptoprocessors that store encryption keys.
Secure enclaves or trusted execution environments isolate sensitive data.
Hardware-based random number generators improve cryptographic strength.

12.2 Secure Boot and TPM

Secure Boot ensures only trusted software loads during startup.
TPM verifies software integrity and stores credentials securely.
Protects against rootkits and boot-level malware.

12.3 Spectre, Meltdown, and Side-Channel Attacks

Vulnerabilities exploiting CPU features like speculative execution and caches.
Allow attackers to read sensitive data by timing or side effects.
Led to major hardware and software patches in recent years.

12.4 Memory Protection and Isolation

Use of Memory Management Units (MMU) to restrict access.
Techniques like Address Space Layout Randomization (ASLR) make attacks harder.
Hardware-enforced sandboxing protects processes from each other.

12.5 Encryption Accelerators (AES-NI, ARM TrustZone)

AES-NI: Intel’s hardware instructions for fast AES encryption.
ARM TrustZone: Secure area of the processor for trusted code.
Accelerators offload cryptographic operations from the CPU, improving speed and security.

13. Real-World Architectures

This section explores popular processor designs used in everyday devices and data centers.

13.1 Intel vs AMD Architecture Comparison

Intel:
- Uses x86 CISC architecture.
- Focus on high single-threaded performance.
- Advanced technologies like Turbo Boost, hyperthreading.
- Strong in laptop, desktop, and server CPUs.
AMD:
- Also x86 but with innovative designs like Zen architecture.
- Competitive multi-core performance at often better price/performance.
- Pioneered chiplet design with Ryzen and EPYC processors.
- Often leads in core count and multi-threaded tasks.

Differences:

AMD has embraced chiplet modularity earlier.
Intel focuses on integrated graphics and hybrid cores (Alder Lake, Raptor Lake).
Both compete fiercely in desktop, server, and laptop markets.

13.2 ARM in Smartphones and IoT

ARM uses a RISC architecture optimized for low power.
Dominates the smartphone market (Apple, Samsung, Qualcomm).
Key for Internet of Things (IoT) devices: sensors, wearables, smart home.
Provides a balance of energy efficiency and performance.
ARM licenses its design to multiple manufacturers, enabling a diverse ecosystem.

13.3 Apple Silicon: M1, M2, M3 Chip Design

Apple designed custom ARM-based chips for Macs and iPads.
Combines CPU, GPU, Neural Engine, and memory on a single SoC (System on Chip).
Features high performance with low power consumption.
Uses big.LITTLE architecture with performance and efficiency cores.
Integrates unified memory architecture for fast data sharing.
M3 (upcoming) expected to use advanced manufacturing (3nm process).

13.4 NVIDIA GPU Architecture

Focused on massively parallel processing.
Thousands of cores optimized for graphics and compute workloads.
Supports AI, deep learning, ray tracing, and gaming.
Uses CUDA cores for general-purpose computing.
Features Tensor Cores specialized for AI matrix operations.

13.5 Google TPUs and AI Chips

Google’s Tensor Processing Units (TPUs) are custom AI accelerators.
Optimized for neural network operations and machine learning workloads.
Used in Google data centers and cloud AI services.
Focus on high throughput and energy efficiency for AI tasks.
Newer TPU generations support training and inference at scale.

14. Future Trends in Computer Architecture

Looking ahead, several exciting technologies will shape computing.

14.1 Chiplets and 3D Chip Stacking

Chiplets: Smaller chip components combined on a single package.
Allows mixing technologies (logic, memory) efficiently.
Improves yield, reduces costs, and boosts performance.
3D stacking vertically stacks layers of chips, shortening data paths.

14.2 AI and ML-Specific Architectures

Processors optimized for AI tasks (matrix multiplication, tensor ops).
Example: Google TPU, NVIDIA’s AI GPUs, dedicated AI accelerators in smartphones.
Increasing use of sparsity-aware and low-precision computation.

14.3 Edge Computing Hardware

Processing data close to its source to reduce latency.
Specialized chips for IoT devices, autonomous cars, and smart cameras.
Emphasizes energy efficiency, real-time response, and security.

14.4 Quantum Architecture Directions

Research into stable, scalable quantum computers.
Developing quantum error correction, qubit connectivity.
Hybrid classical-quantum systems to handle complex problems.

14.5 Neuromorphic and Brain-Inspired Systems

Architectures mimicking neurons and synapses.
Aim to replicate brain efficiency and parallelism.
Potential for AI, pattern recognition, sensory processing.
Still experimental but promising for future AI breakthroughs.

14.6 Open Hardware and RISC-V Revolution

RISC-V: An open, royalty-free ISA encouraging innovation.
Growing ecosystem of chips, tools, and operating systems.
Enables customizable and secure hardware designs.
Potential to disrupt traditional proprietary processor markets.

15. Conclusion

This final section summarizes what you’ve learned, highlights the importance of understanding computer architecture, explores career opportunities, and suggests resources for further study.

15.1 Summary of Key Concepts

Computer architecture is the blueprint of how computers are designed and how they work inside.
Key components include the CPU, memory systems, storage, input/output, and control units.
Understanding performance metrics, parallelism, and optimization helps design efficient systems.
Modern architectures blend hardware and software innovations to meet evolving needs.
Emerging trends like AI-specific chips, quantum computing, and open hardware point to the future.

15.2 How Understanding Architecture Empowers You

Enables you to write more efficient software by knowing hardware limits.
Helps in troubleshooting and optimizing system performance.
Opens doors to innovate and improve computer designs.
Provides a foundation for learning advanced fields like AI, cybersecurity, and embedded systems.
Gives insight into how technology evolves and what skills will be in demand.

15.3 Career Paths in Computer Architecture

Hardware Design Engineer: Designing processors, memory systems, and chipsets.
Firmware Developer: Writing low-level software that controls hardware.
Systems Architect: Planning and optimizing complex computing systems.
Performance Engineer: Analyzing and improving system efficiency.
Research Scientist: Exploring new paradigms like quantum and neuromorphic computing.
Roles in AI hardware development, cloud infrastructure, and IoT device engineering also rely heavily on architectural knowledge.

Techlivly

1. Introduction to Computer Architecture

1.1 What Is Computer Architecture?

1.2 Why Understanding Architecture Matters

1.3 Evolution of Computer Systems

1.4 Von Neumann vs Harvard Architectures

Von Neumann Architecture

Harvard Architecture

1.5 Modern Trends in Architecture (e.g., RISC-V, AI Chips)

RISC-V (Reduced Instruction Set Computer – V)

AI Chips (Accelerators like TPUs, NPUs)

Other Trends:

✅ Summary of Section 1:

2.2 Input Devices

2.3 Output Devices

2.4 System Bus (Address, Data, Control)

There are 3 types of buses:

2.5 Storage Hierarchy Overview

Pyramid of Storage:

✅ Summary of Section 2:

3. Central Processing Unit (CPU)

3.1 Anatomy of a CPU

3.2 Control Unit (CU)

3.3 Arithmetic Logic Unit (ALU)

3.4 Registers and Their Types

3.5 Instruction Cycle: Fetch, Decode, Execute

1. Fetch

2. Decode

3. Execute

Cycle Repeats

3.6 Clock Speed and Performance

✅ Summary of Section 3:

3. Central Processing Unit (CPU)

3.1 Anatomy of a CPU

Key Parts:

Simple Analogy:

3.2 Control Unit (CU)

Functions:

Example:

3.3 Arithmetic Logic Unit (ALU)

Responsibilities:

Real-world Analogy:

3.4 Registers and Their Types

Key Types of Registers:

Why They’re Important:

3.5 Instruction Cycle: Fetch, Decode, Execute

Step 1: Fetch

Step 2: Decode

Step 3: Execute

Then the cycle repeats…

3.6 Clock Speed and Performance

Measured in:

But performance isn’t only about speed:

Example:

✅ Summary of Section 3

4. Memory and Storage Systems

4.1 RAM vs ROM

RAM (Random Access Memory)

ROM (Read-Only Memory)

4.2 Cache Memory: L1, L2, L3

Purpose:

Levels:

4.3 Virtual Memory and Paging

Virtual Memory:

Paging:

4.4 Secondary Storage: HDDs and SSDs

HDD (Hard Disk Drive):

SSD (Solid State Drive):

4.5 Flash Memory and Cloud Storage

Flash Memory:

Cloud Storage:

4.6 Memory Management Unit (MMU)

Key Functions:

✅ Summary of Section 4:

5. Instruction Set Architecture (ISA)

5.1 What Is an ISA?

5.2 RISC vs CISC

RISC (Reduced Instruction Set Computer)

CISC (Complex Instruction Set Computer)

5.3 Common ISAs: x86, ARM, MIPS, RISC-V