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Context:

Differences between CPUs, GPUs, and TPUs, and how each processor type contributes uniquely to computing and artificial intelligence tasks.​

Details

Processing units are like the brain of a computer. They handle all the tasks a computer needs to do, like solving math problems, taking pictures, or sending texts. Without processing units, a computer wouldn’t be able to function. These units help the computer perform different operations, just like our brain helps us think and act.

About CPU (Central Processing Unit)

A CPU is the brain of a computer. It handles all kinds of tasks, from opening apps to doing math problems. CPUs have "cores," which are like workers inside the CPU. Each core can do one task at a time. Modern CPUs usually have between 2 to 16 cores.

About GPU (Graphics Processing Unit)

It is designed to handle many tasks at once, not one after another. GPUs are excellent for things like video games, where they create realistic graphics by handling millions of tiny tasks at the same time.

They are now used in AI and machine learning because they can process large amounts of data quickly. Still, GPUs need help from CPUs for certain jobs because CPUs are better at managing tasks step-by-step.

TPU (Tensor Processing Unit)

A TPU is even more specialized than a GPU. Google created TPUs in 2015 specifically to run AI models faster and better. These chips focus on "tensor operations," which are super important for training AI systems. For example, training a big AI model might take weeks on a GPU, but with a TPU, it could finish in hours. TPUs power services like Google Search, YouTube, and DeepMind’s language models.

How Are They Different?

Purpose

• CPUs are general-purpose and good at handling everyday tasks.
• GPUs are great at multitasking and working on huge datasets, especially for graphics or AI.
• TPUs are hyper-focused on AI tasks and beat both CPUs and GPUs when it comes to training AI models.

Speed

• CPUs work well for regular computing but are slower for heavy tasks.
• GPUs are much faster for parallel tasks, like generating images or solving complex math problems.
• TPUs are the fastest for AI-related calculations, cutting down training times dramatically.

Flexibility

• CPUs are flexible and can do almost anything, but they aren’t always efficient.
• GPUs are less flexible than CPUs but still versatile enough for many uses beyond gaming.
• TPUs are very narrow in their focus—they’re built only for AI and don’t do other tasks well.

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Source:

INDIAN EXPRESS

https://t.me/+hJqMV1O0se03Njk9