Computers help human beings with every possible job. They are more efficient, fast, and precise. In recent decades, we have witnessed commendable growth in technology, and after the influence of Artificial Intelligence and Machine Learning, computers are more powerful and more accurate than before.
As technology advances, the hardware used in computer systems is upgraded to meet the demands. Having the required hardware for the specific type of usage can really make things easier and faster. There was the CPU first, followed by the GPU based on its needs, and now we have the TPU. As computing demands and expectations grow, we will see a plethora of new hardware to meet those demands and expectations. In this article, we will know the difference between the CPU and GPU vs TPU.
What is a CPU?
The CPU is the abbreviation for Central Processor Unit is the unit that carries out most processing inside a computer. It is the head and heart of the computer. The software that runs on the PC and other device components processes all instructions and acts in the form of a strong computer. The CPU comprises two typical components, which are the Control Unit and the Arithmetic Logical unit (ALU).
We give instructions to the computer in the language we understand, but because the computer only understands binary, the instructions must be converted to binary. Now, the Control Unit plays a very important part here. The control unit extracts the instructions and decodes/converts them to a language that the computer can understand, i.e, a binary language. After decoding the instruction, it executes it.
It also instructs the ALU on what to do, and once the execution of the instruction is finished, it converts it into a human-readable language. The ALU performs all the mathematical and logical operations and follows the instructions of the control unit. The cost of the CPU depends on the number of cores, threads, etc. This is why most of the processors under 10,000 are dual core processors.
What is a GPU?
The GPU is the abbreviation for Graphics Processing Unit. It is specially designed to process graphics. The Graphics processing unit (GPU) provides a creative side to the logical thought area of a computer’s silicon brain, which helps make graphical user interfaces aesthetically pleasing icons and designs, not black/white lines. Although a large number of computers are fitted with a built-in GPU to ensure that Windows is displayed on the attached screen, there are countless graphics-based tasks, like video rendering and CAD, which often need a specialized or discrete GPU.
Mostly, Graphics Processing Units are used by gamers to obtain high graphics and a smooth gaming experience. The GPU’s main functionality is to handle rigorous graphics rendering. Video editing is another prominent GPU use. This is especially true when you work with huge amounts of high-resolution media such as 360° or 4K films. This is why a high-end GPU is really beneficial. A more powerful GPU means high and smooth graphics and high video quality. If you are Interested in buying a GPU then we have cumulated a list of best graphics cards under 10000 and also under 15000 based on your budget. Do check them out!
What is a TPU?
The TPU is an abbreviation for Tensor Processing Unit. It is a custom developed applications- specific integrated circuits (ASICs) by Google. In 2015, Google started using TPU and made it public by 2018. It is designed to handle excessive workloads to boost AI algorithms and calculations. TPUs have been built on the basis of Google’s profound experience and machine-learning leadership.
In heavy vector and matrix calculations, TPUs are quite quick. The performance of linear algebra computations is accelerated by TPU resources, which are widely utilized in machine learning applications. When training large, complicated neural network models, TPUs minimise time and accuracy. Models that have been training for weeks on other hardware platforms may converge in hours on TPUs. In other words, TPU accelerates the process of developing machine learning and Deep Learning models using Tensorflow, which is a programming framework which provides various tools and libraries.
CPU vs GPU vs TPU
The CPU, GPU and TPU are very different from each other. The variation between the CPU, GPU, and TPU is that the CPU handles all of the computer logic, calculations, and input/output. In comparison to the GPU built into the CPU, the GPU is an additional processor for improving the graphics interface and running high-end operations. TPUs are powerful custom processors for executing a project in a Tensorflow framework.
A powerful Central Processing Unit will ensure the smooth execution of tasks and programs. A powerful Graphics Processing Unit will enable you to enjoy smooth and high-end graphics and video quality. A powerful TPU will allow you to accelerate calculations and algorithms for Artificial Intelligence, Machine Learning, and Deep Learning.
The CPU, GPU, and TPU are three different types of processing units.For the overall performance of the computer, the CPU is responsible. For delivering high-end graphics and video quality, the GPU is responsible. Along with the CPU, the GPU is a piece of additional hardware.TPU is used in the field of Artificial Intelligence, Machine Learning, and Deep Learning. Each of the three processing units has its own set of functions.This article may have helped you understand the distinctions between the CPU, GPU, and TPU.