Training AI workloads

What types of CPUs are used to train AI workloads?

Training AI workloads, especially those involving deep learning and large models, requires significant computational power. Various types of CPUs and processing units are used, each with its own set of advantages for different aspects of AI training and inference. Here's a brief overview of the main types of processing units used:

  1. Central Processing Units (CPUs):

    • Traditional CPUs are versatile and capable of performing a wide range of tasks. While not as efficient for the parallel processing requirements of most AI and machine learning workloads compared to GPUs and TPUs, they are still used for certain tasks that require sequential processing or where low-latency, high-throughput inference is needed.
  2. Graphics Processing Units (GPUs):

    • GPUs are specialized hardware capable of handling multiple operations in parallel, making them highly effective for the matrix and vector computations that are common in deep learning. Their architecture allows for significant acceleration of AI workload training and inference, making GPUs a popular choice for deep learning tasks.
  3. Tensor Processing Units (TPUs):

    • Developed by Google, TPUs are application-specific integrated circuits (ASICs) designed specifically for neural network machine learning. They are optimized for TensorFlow, Google's machine learning framework, and are designed to accelerate both the training and inference phases of deep neural networks. TPUs are known for their high throughput and efficiency in processing AI workloads.
  4. Field-Programmable Gate Arrays (FPGAs):

    • FPGAs are integrated circuits that can be configured by the customer or designer after manufacturing—hence "field-programmable." They are used in AI for their flexibility and efficiency, offering a good balance between the programmability of software and the high performance of hardware. FPGAs can be optimized for specific AI algorithms, offering efficient performance for both training and inference tasks.
  5. Advanced Processing Units (APUs):

    • APUs are a type of microprocessor that combines a CPU and a GPU on a single chip. They are designed to improve the efficiency of processing graphics and other parallel processing tasks, making them suitable for certain AI and machine learning applications that can leverage both types of processing power.

Each type of processing unit has its specific strengths and applications within AI workload management. The choice between them depends on the specific requirements of the task at hand, including the need for parallel processing, the size of the model, the efficiency and cost considerations, and the phase of AI workload (training vs. inference). GPUs and TPUs are currently the most widely used for training deep learning models due to their high efficiency in parallel processing and specialized architectures for AI computations.