Classification of Computer based on Architecture

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Computers can be classified based on their architecture, which refers to the underlying design and organization of the computer system. Here are some common classifications based on architecture:

1.Von Neumann Architecture:

  1. Named after computer scientist John von Neumann.
  2. It features a single memory for both data and instructions.
  3. Instructions and data are stored in the same memory space.
  4. It follows a sequential execution model, where instructions are fetched, decoded, executed, and then stored.

2.Harvard Architecture:

  1. Named after Harvard University.
  2. It has separate memories for data and instructions.
  3. Allows simultaneous access to both instruction and data memory, which can lead to improved performance.
  4. Often used in embedded systems and microcontrollers.

3.RISC (Reduced Instruction Set Computing):

  1. Emphasizes a small, highly optimized set of instructions.
  2. Instructions are simple and execute in a single clock cycle.
  3. Typically found in microcontrollers, mobile devices, and embedded systems.
  4. Examples include ARM processors.

4.CISC (Complex Instruction Set Computing):

  1. Supports a large and complex set of instructions.
  2. Instructions can perform multiple low-level operations, reducing the number of instructions needed for a task.
  3. Often found in desktop and server processors.
  4. Examples include x86 architecture (Intel and AMD processors).

5.Superscalar Architecture:

  1. Allows multiple instructions to be executed simultaneously.
  2. Exploits instruction-level parallelism to improve performance.
  3. Commonly used in modern desktop and server processors.

6.Parallel Processing Architectures:

  1. Utilizes multiple processing units (cores) to execute instructions concurrently.
  2. Examples include multi-core processors and multi-processor systems.
  3. Designed to improve performance by dividing tasks among multiple processing units.

7.Vector Processing Architectures:

  1. Optimized for operations on arrays or vectors of data.
  2. Execute multiple operations simultaneously using vector instructions.
  3. Often used in scientific computing and multimedia applications.

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