CPU : Several core , Low latency , Serial processing , Limited simultaneous operations , Large memory capacity .
GPU : Thousands of Cores , High data throughput , Massive parallel computing , Limited multitasking Low memory .
TPU : Matrix based workload , High latency, High data throughput , Suited for large batch sizes, Complex neural network models . (1)
CPU (Central Processing Unit), GPU (Graphics Processing Unit), and TPU (Tensor Processing Unit) are three types of processors, each with its unique capabilities and use cases. Understanding the differences between them can help in choosing the right hardware for specific tasks:
CPU (Central Processing Unit)
General Purpose: CPUs are versatile and can handle a wide range of computing tasks. They are designed to be good at executing a broad set of instructions.
Architecture: Consists of a few cores optimized for sequential serial processing.
Use Cases: Ideal for tasks that require general-purpose processing, including running the operating system, desktop applications, and server tasks.
Performance: While CPUs can handle almost any task, they are not always the fastest option for tasks that can be parallelized.
GPU (Graphics Processing Unit)
Specialized for Graphics: Originally designed to accelerate graphics rendering.
Parallel Structure: Consists of hundreds or thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.
Use Cases: Ideal for tasks that can be parallelized, such as video rendering, 3D graphics, and scientific simulations. GPUs are also widely used in machine learning and deep learning for their efficient handling of matrix and vector operations.
Performance: Significantly faster than CPUs at certain tasks, particularly those involving large-scale matrix or vector operations.
TPU (Tensor Processing Unit)
AI and Machine Learning: Developed by Google, TPUs are specifically designed for neural network machine learning.
Architecture: Optimized for a specific set of machine learning operations, particularly those involving tensors (multi-dimensional arrays).
Use Cases: Primarily used for deep learning tasks in large data centers. They are highly efficient for training and inference in neural networks.
Performance: Can outperform CPUs and GPUs for certain machine learning tasks due to their specialized architecture, but they are not as versatile.
Summary
CPUs are jack-of-all-trades and master of general computation, suitable for a wide range of tasks but not always the most efficient for specialized tasks.
GPUs excel in parallel processing, making them ideal for graphics, video processing, and certain types of scientific computations, as well as machine learning.
TPUs are specialized for machine learning and deep learning, offering high performance for specific tasks but are less versatile than CPUs and GPUs.
The choice between a CPU, GPU, and TPU depends largely on the specific needs of the application or task at hand. CPUs are suitable for general-purpose computing, GPUs are preferred for tasks that can be efficiently parallelized, and TPUs are optimal for specific machine learning and deep learning applications.
Sources :
(1) -https://www.linkedin.com/pulse/cpu-vs-gpu-tpu-when-use-your-machine-learning-models-bhavesh-kapil