Accelerating the AI ​​world: a super battle between CPU, GPU and TPU!

When it comes to CPUs, GPUs, and TPUs, we are usually talking about the differences in computer processing power and performance. Let's describe their differences in layman's terms:

CPU (Central Processing Unit): The CPU is the brain of the computer, responsible for executing the basic instructions and tasks of the computer. It can handle a variety of different types of tasks, such as browsing the web, running office software, and managing the operating system. The CPU has multiple cores, each of which can handle a task, but is relatively slow when handling complex tasks.

Like an all-around office worker who can do many different kinds of work, but can be a little slow when dealing with large amounts of paperwork.

GPU (Graphics Processing Unit): GPU was originally designed to handle graphics and image-related tasks, such as displaying video games and rendering 3D scenes. Compared with CPU, GPU has more small processing cores, which makes it excel in processing large-scale data in parallel. Therefore, GPU is suitable for many parallel computing tasks, such as deep learning, cryptography, and scientific simulation.

The GPU is like a team of little soldiers that excels at handling multiple similar tasks at the same time, such as cleaning an entire building.

TPU (Tensor Processing Unit): TPU is an accelerated processor developed by Google specifically for artificial intelligence tasks. It is specifically optimized for tensor operations, which are matrix operations common in deep learning. TPU is very efficient in performing these specific tasks and is faster than both CPU and GPU. However, TPU may not perform as well as CPU or GPU on other general computing tasks.

The TPU is like a math genius that is particularly good at processing matrix operations, but may not be so good at other things.

For example, let's say we have a large deep learning model that recognizes objects in photos. Using a CPU to run this model might take hours to complete because the CPU is relatively slow to process a photo. Using a GPU can significantly reduce the processing time, perhaps to just a few minutes. But if we have a TPU, running the same task might only take a few seconds because the TPU is specifically designed to handle deep learning tasks and is very efficient for tasks involving a lot of matrix operations.

CPU, GPU and TPU are different types of computer processors. They differ in design and purpose, and are not mutually inclusive or composed. Therefore, different processors have their own advantages and applicability in different tasks and applications.

Source: Chongqing Radio Science Popularization Experience Center

Audit expert: Zhang Qiyi

Statement: Except for original content and special notes, some pictures are from the Internet. They are not for commercial purposes and are only used as popular science materials. The copyright belongs to the original authors. If there is any infringement, please contact us to delete them.