artificial intelligence infrastructure; What is the role of GPU?

Several decades have passed since the emergence of artificial intelligence; However, until recently, limited access to suitable computing infrastructure for big data was one of the obstacles to further progress in this field. Currently, businesses and IT executives are making significant investments in AI-related technologies. Artificial intelligence is rapidly conquering different fields; It is expected that soon organizations will use artificial intelligence to create a more effective and efficient strategy at the macro level.

For this reason, experts are giving special value and importance to some key aspects of artificial intelligence and are trying to expand the necessary infrastructure for it. The cost of the infrastructure required for artificial intelligence is one of the most important factors that have forced experts to seek more economical and competitive solutions.

Types of artificial intelligence hardware

The hardware used in artificial intelligence today mainly includes one or more of the following examples;

CPU- central processing units

GPU- Graphics processing units

FPGA – Field Programmable Gate Arrays

ASIC- application specific integrated circuits

Using a combination of powerful multi-core CPUs, modern machines can perform parallel processing with dedicated hardware. GPU and FPGA are popular and dedicated hardware in artificial intelligence systems. An FPGA is not a processor, so it cannot execute a program stored in memory. In contrast, a GPU is a chip designed to speed up the processing of multidimensional data such as images. Repetitive operations that need to be applied to different parts of the input, such as texture mapping, image rotation, translation, etc., are performed much more efficiently using a GPU that has dedicated memory.

Graphics processing units or GPUs are specialized hardware that are increasingly used in machine learning projects. Since 2016, the use of GPUs for artificial intelligence is growing rapidly. These processors have been widely used in facilitating deep learning, education and automated vehicles.

GPUs are increasingly being used to accelerate artificial intelligence; For this reason, GPU manufacturers are trying to use special neural network hardware to increase the development process and progress of this field. Major GPU developer companies, such as Nvidia NVLink, are trying to increase the capabilities of these processors to transfer larger amounts of data.

Necessary infrastructure for artificial intelligence

High storage capacity, network infrastructure and security are the most important infrastructures required for artificial intelligence. In the meantime, there is another important and determining factor; High computing capacity. To fully and optimally take advantage of the opportunities offered by artificial intelligence, organizations need resources for efficient computing performance, such as CPUs and GPUs. CPU-based environments can be suitable for early AI workloads. But deep learning involves multiple large data sets as well as scalable neural network algorithms. That is why in such conditions, the CPU may not perform ideally. In contrast, GPU can accelerate deep learning up to 100 times compared to CPU. After all, the capacity and density of calculations will also increase and the demand for networks with better performance and more storage space will increase.

AI chips work by combining lots of small transistors that are much faster and more efficient than larger transistors. Artificial intelligence chips must have certain characteristics;

• Perform a large number of calculations in parallel
• Low precision, but successful number crunching for AI algorithms
• Easy and fast memory access by storing the entire AI algorithm in a single chip
• Using special programming languages ​​to efficiently translate codes to run on the AI ​​chip

Different types of artificial intelligence chips are used for different tasks. GPUs are mostly used for initial development and refinement of artificial intelligence algorithms. FPGA is mostly used to use artificial intelligence algorithms in real world data input. ASICs can also be used for training or inference.

Comparison of GPU and CPU as two essential infrastructures

CPUs have multiple complex cores that work sequentially with a small number of computing threads. While GPUs have a large number of simple cores and can perform calculations in parallel and simultaneously through thousands of calculation threads.

In deep learning, the host code runs on the CPU and the CUDA code runs on the GPU.

Unlike CPUs, GPUs perform more complex tasks such as 3D graphics processing, vector calculations, etc. better and faster.

– CPU can handle long complex tasks optimally, but GPU may suffer from low bandwidth problem. This means that transferring large amounts of data to the GPU may be slow.

High bandwidth, low latency and programmability make GPU much faster than CPU. In this way, the CPU can be used to train a model where the data is relatively small. GPU is suitable for training deep learning systems in the long term and for very large datasets. CPU can train a deep learning model slowly; While GPU accelerates model training.

GPU was originally designed to implement graphics lines. Therefore, the computational cost of using deep learning models is very high. Google has unveiled a new initiative called TPU (tensor processing unit) and it aims to improve the disadvantages of GPU. Tensor kernels are intended to speed up the training of neural networks.

According to studies, the demand for artificial intelligence chipsets will increase by 10 to 15 percent by 2025. With the computing power, development ecosystem, and data availability, chip manufacturers can increase production of the necessary AI hardware by 40-50%; And this is a golden era in the last few decades.