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.
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.