For this reason, experts They attach particular value and importance to key aspects of artificial intelligence and are working to broaden the infrastructure needed for it. The cost of the infrastructure needed for artificial intelligence is one of the most important factors that have forced professionals to look for more economical and competitive solutions.
Types of artificial intelligence hardware
Hardware used in Today's AI mainly consists of one or more of the following:
GPU- Graphics Processing Units
FPGA- Field Programmable Gateway Arrays
ASIC-Program Integrated Circuits
Modern machines using a combination of CPUs Powerful multi-cores can do parallel processing with dedicated hardware. GPUs and FPGAs are popular and proprietary hardware in artificial intelligence systems. FPGA is not a processor, so it cannot run a program stored in memory. In contrast, the GPU is a chip designed to speed up the processing of multidimensional data such as images. Duplicate operations to be performed on different parts of the input, such as texture mapping, image rotation, translation, etc., are performed much more efficiently using a dedicated memory GPU.
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 has been growing rapidly. These processors have been widely used to facilitate deep learning, training, and automated vehicles. GPUs are increasingly being used to accelerate artificial intelligence; For this reason, GPU manufacturers are trying to use neural network-specific hardware to increase the development and progress of this field. Large GPU developers, such as Nvidia NVLink, are trying to increase the capabilities of these processors to transfer more data.
Infrastructure required for intelligence Artificial
High storage capacity, network infrastructure and security are among the most important infrastructures needed for artificial intelligence. There is another important and determining factor: high computing capacity. To make the most of the opportunities offered by artificial intelligence, organizations need resources to efficiently calculate performance, such as CPUs and GPUs. CPU-based environments can be suitable for the initial loads of artificial intelligence. But deep learning involves a collection of multiple large data sets as well as scalable neural network algorithms. This is why in such cases, the CPU may not perform ideally. In contrast, the GPU can accelerate deep learning up to 100 times faster than the CPU. In addition, computing capacity and density will increase, and demand for better-performing networks and more storage space will increase.
Intelligence chips Synthetics work by combining a large number of small transistors, which are much faster and more efficient than larger transistors. Artificial intelligence chips must have certain characteristics:
- Perform a large number of calculations in parallel
- Calculate numbers with low but successful accuracy for AI algorithms
- Easy and fast access to memory with Store the entire AI algorithm on a single chip
- Use special programming languages to effectively translate code to run on the AI chip
A variety of artificial intelligence chips are used for a variety of tasks. GPUs are mostly used for the initial development and modification of artificial intelligence algorithms. FPGA is mostly used to use artificial intelligence algorithms to input real-world data. ASICs can also be used for training or conclusions.
Comparison of GPU and CPU as two essential infrastructures
- CPUs have multiple complex cores with a number of Few computational disciplines work sequentially. While GPUs have a large number of simple cores and can perform calculations in parallel and simultaneously through thousands of computational strings.
Deep learning, host code runs on the CPU and CUDA code runs on the GPU.
- Unlike CPUs, GPUs run more complex tasks such as 3D graphics processing, computing It performs better and faster. The band is low facing. This means that transferring large amounts of data to the GPU may be slow.
High bandwidth, low latency, and programming capabilities make the GPU much faster than the CPU . In this way, the CPU can be used to teach a model where the data is relatively small. The GPU is suitable for training deep learning systems in the long run and for very large datasets. The CPU can easily teach a deep learning model; While the GPU speeds up model training.
The GPU was originally designed to implement graphic 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 intends to improve the shortcomings of the GPU. Tensor cores are designed to speed up the training of neural networks.
According to studies, the demand for artificial intelligence chipsets by 2025 is approx. It increases by 10 to 15%. Despite their computing power, development ecosystem, and data availability, chipmakers can increase the production of hardware required by artificial intelligence by 40 to 50 percent; And this is the golden age of the last few decades.