Several decades have passed since the emergence of artificial ielligence; However, uil recely, limited access to suitable computing infrastructure for big data was one of the obstacles to further progress in this field. Currely, businesses and IT executives are making significa investmes in AI-related technologies. Artificial ielligence is rapidly conquering differe fields; It is expected that soon organizations will use artificial ielligence to create a more effective and efficie strategy at the macro level.
For this reason, experts are giving special value and importance to some key aspects of artificial ielligence and are trying to expand the necessary infrastructure for it. The cost of the infrastructure required for artificial ielligence is one of the most importa factors that have forced experts to seek more economical and competitive solutions.
Types of artificial ielligence hardware
The hardware used in artificial ielligence today mainly includes one or more of the following examples;
CPU- ceral processing units
GPU- Graphics processing units
FPGA – Field Programmable Gate Arrays
ASIC- application specific iegrated 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 ielligence systems. An FPGA is not a processor, so it cannot execute a program stored in memory. In corast, 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 differe parts of the input, such as texture mapping, image rotation, translation, etc., are performed much more efficiely 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 ielligence 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 ielligence; For this reason, GPU manufacturers are trying to use special neural network hardware to increase the developme 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 amous of data.
Necessary infrastructure for artificial ielligence
High storage capacity, network infrastructure and security are the most importa infrastructures required for artificial ielligence. In the meaime, there is another importa and determining factor; High computing capacity. To fully and optimally take advaage of the opportunities offered by artificial ielligence, organizations need resources for efficie computing performance, such as CPUs and GPUs. CPU-based environmes 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 corast, 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 efficie than larger transistors. Artificial ielligence 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 eire AI algorithm in a single chip
- Using special programming languages to efficiely translate codes to run on the AI chip
Differe types of artificial ielligence chips are used for differe tasks. GPUs are mostly used for initial developme and refineme of artificial ielligence algorithms. FPGA is mostly used to use artificial ielligence algorithms in real world data input. ASICs can also be used for training or inference.
Comparison of GPU and CPU as two esseial infrastructures
CPUs have multiple complex cores that work sequeially 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 amous 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 impleme 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 disadvaages of GPU. Tensor kernels are iended to speed up the training of neural networks.
According to studies, the demand for artificial ielligence chipsets will increase by 10 to 15 perce by 2025. With the computing power, developme 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.
nn
