Differences between GPU and CPU in the area of artificial intelligence

Artificial intelligence is in vogue and there is much talk about machine learning and deep learning systems, methods whose intention is to make computers approach the way in which the human brain processes information and, based on said information, generate oral content. understandable by anyone, make their own decisions, make forecasts or produce new knowledge that at the same time is highly accurate.

Likewise, its implementation in machines by means of conventional CPUs or GPUs, the modern version of the graphics cards of yesteryear, is a trend. So we give you a brief description of both hardware components.

CPU (central processing unit) and GPU (graphics processing unit) are two types of information processors used in computers. In the first case, it executes the sequential instructions that make up a program or application, as well as the processes within the operating system that hosts them, and there are enough computing power to carry out the usual tasks of current computing. .

On the other hand, GPUs, as the latest generation graphics cards are called, have been linked from their origins to the visual component of computing, transforming data into visible information through the screen, thus lightening the CPU workload. However, they have evolved favorably thanks to the development of 3D video games and the increasing visual demands of fans of this activity, to such an extent that their current processing power is similar to that of conventional CPUs.

How are the CPU and GPU used in artificial intelligence systems?

First of all, both are hardware components that at the highest and lowest levels are manufactured with the same inputs. They have cores and internal memory, among other related elements. However, its architecture must be taken into account.

Indeed, CPUs have multiple cores in their makeup but are coupled for serial processing, making them ideal for running multiple tasks simultaneously, including machine learning systems.

In contrast, in the architecture of GPUs, the cores are designed to work in parallel and hundreds or thousands of them can coexist. This means that with the same data sample, the processing work can be distributed among all of them, obtaining a better overall performance.

But the latter also means that GPUs are more predisposed to work with artificial intelligence models that use deep learning algorithms, since this technique is characterized by processing information by layers of neural networks, simulating the mechanism used by the human brain. to learn something new. Each layer processes a group of data that is optimized by training and is connected to the previous one so that learning is possible.

Image by Colin Behrens from Pixabay

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