In this blog post, we will be exploring the difference between CPU vs GPU vs TPU.
Initially, the computers only had a CPU, then GPU came into existence, and now TPU. As the tech industry is evolving, and discovering brand-new ways to use computers, the demand for faster hardware is gradually increasing.
But what is the difference between CPU Vs GPU vs TPU?
To get answer to this question, you are requested to completely read this article
So let’s start our discussion by comparing CPU vs GPU vs TPU briefly.
CPU vs GPU vs TPU
Basically, the difference between CPU, GPU, and TPU is that the CPU is a general-purpose processor that manages and handles all the logics, calculations, and I/O of the computer. Whereas a GPU is an additional processor to improve the graphical interface and run high-end tasks. TPUs are powerful custom-made processors to operate the project made on a particular framework, i.e. TensorFlow.
Hope you got an overview of What CPU, GPU and TPU is.
Now, let us understand all the three processing units in depth.
What is CPU?
CPU, an abbreviation for the central processing unit is the brain of a computer that manages all the functions of a computer.
A CPU executes the instruction for computer programs. It handles all the primary arithmetic, logic, controlling, and input/output functions of the program.
CPU is one of the major component that operates the operating system by continuously obtaining inputs and presenting output to the users.
A CPU consists of at least one processor. The processor is an existent chip inside the CPU that performs or executes all the calculations. For several years, CPUs had just one processor, however now dual-core CPUs (CPU with two processors) are quite common.
Additionally, you can also see the quad processors CPUs and octa processors CPUs in the market.
Some of the famous Manufacturers of CPUs are Intel, AMD, Qualcomm, NVIDIA, IBM, Samsung, Hewlett-Packard, VIA, etc.
What is GPU?
GPU stands for Graphical Processing Unit and is integrated into each CPU in some form. However, a few of the tasks and applications that need extensive visualization can’t be handled by an inbuilt GPU. Tasks like machine learning, computer-aided design, video games, video editing, live streamings, and data scientist.
For performing high-end rendering and visualization tasks, the GPU is used.
Moreover, if you want to perform extensive graphical tasks, however, don’t want to invest in a physical GPU, you can rent a GPU server.
Some of the famous Manufacturers of GPUs are NVIDIA, AMD, Broadcom Limited, etc.
What is TPU?
TPU stands for Tensor Processing Unit, which is an application-oriented integrated circuit, to boost the AI calculations and algorithm. Google designs and develops it particularly for neural network machine learning for the TensorFlow software. Google owns TensorFlow software.
Google began using TPU in 2015 and by 2018 they made it public.
TPUs are tailor crafted for a particular app framework. I.e TensorFlow, an open-source machine learning platform, with great tools, libraries, and community that will help users quickly build and deploy ML apps.
Cloud TPU lets you run your machine learning projects on TPU with the help of tensor flow. Built for exceptionally powerful performance and flexibility, Google’s TPU helps developers and researchers to operate models with high-level Tensor Flow APIs.
The models that used to take weeks to train on GPU or other hardware can merely be converged within hours on TPU.
TPU is used only for TensorFlow projects by developers and researchers. Only Google makes TPU.
I hope this article on CPU vs GPU vs TPU may help you identify the difference between CPU vs GPU vs TPU.
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