1. Core Design:

  • AI Processors: They often have a greater number of simpler cores optimized for parallel processing. These cores may be less powerful individually but are designed to execute a high volume of similar operations simultaneously.
  • CPUs: CPUs usually have fewer, more complex cores optimized for sequential task execution and can handle a wide range of instructions.

2. Memory Architecture:

  • AI Processors: These processors typically have high-bandwidth memory (HBM) integrated directly onto the processor or very close to it, which reduces latency and increases the speed at which data can be fed to the processor cores.
  • CPUs: CPUs use a hierarchical memory structure that includes caches (L1, L2, L3) close to the core, with RAM further away. This setup is optimized for a mix of tasks with varying memory access patterns.

3. Data Pathways:

  • AI Processors: They have data pathways designed to accommodate the flow of large amounts of data necessary for tasks such as matrix multiplication, which is common in AI computations.
  • CPUs: The data pathways in CPUs are designed for more general-purpose use and are optimized to handle a variety of data types and operations.

4. Instruction Sets:

  • AI Processors: They may incorporate specialized instruction sets for operations common in machine learning, such as tensor operations, convolutions, and activation functions.
  • CPUs: CPUs support a broad set of instructions to handle various types of software and tasks, including complex branching and decision-making operations.

5. Interconnects:

  • AI Processors: High-speed interconnects are often used to link multiple AI processors together, allowing for scalability and the distribution of large workloads across multiple chips.
  • CPUs: While CPUs can also be linked together, the interconnects are typically designed for a balance between data transfer speed and compatibility with a wide range of peripherals and I/O operations.

6. On-chip Integration:

  • AI Processors: Some AI chips integrate other functions such as memory and digital signal processors (DSPs) onto the same chip, which can help reduce latency.
  • CPUs: Integration on CPUs is less about specific task optimization and more about general computation, with a focus on flexibility.

7. Thermal Design:

  • AI Processors: The thermal design of AI processors is geared towards maintaining performance under the high workload of continuous operations.
  • CPUs: CPUs have dynamic thermal design to accommodate fluctuating workloads, with power gating and other features to manage heat when the processor is not under full load.

8. Fabrication Process:

  • AI Processors: They may be fabricated using processes that allow for high transistor density, which is crucial for parallel processing capabilities.
  • CPUs: Modern CPUs also use advanced fabrication processes for transistor density but prioritize versatility in processing different types of instructions efficiently.