GPUs for Artificial Intelligence: Teraflops and CUDA - Digimax

GPUs have been one of the key factors that have enabled the disruptive development of artificial intelligence in recent years: they are used to process large amounts of data, necessary for industrial automation and artificial vision applications. In essence, these are highly specialized processors that can perform intensive and parallel calculations very quickly and, at the same time, handle a large amount of data.

The use of GPUs has allowed AI developers to create much more complex and efficient artificial neural networks, far more advanced than traditional processors. This has led to rapid progress in the learning speed and performance of AI, opening up new possibilities for industrial automation. Let's find out together the most recent news related to embedded PCs for artificial intelligence, talking about the most innovative GPU processors in terms of Teraflops and CUDA.

CPU vs GPU: the differences between the two processors

To understand the technological evolution that has led to the development of modern AI solutions, it is essential to know the differences between CPU (Central Processing Unit) and GPU (Graphics Processing Unit): two types of processors that operate in different ways.

Typically, the CPU is the core component of a PC and handles input processing, data storage, and workflow control. A CPU is equipped with a few cores, each highly specialized in different operations: for this reason a standard CPU is able to perform a few tasks but quickly and accurately.

On the other hand, the GPU can perform a large number of tasks simultaneously: it has many cores specialized in different modes of computation and can handle large amounts of data in parallel. In the past, GPUs have been used to process images and video graphics, but with the advent of deep learning, they are taking on a crucial role for AI.

Indeed, today there are GPUs for AI equipped with high efficiency in the parallel processing of large data sets, significantly improving performance compared to traditional CPUs and accelerating calculation operations to manage artificial intelligence processes.

Teraflops: high computing power of GPUs

To evaluate the computing power of GPUs, a measurement system called FLOPS, an acronym for Floating point operations per second, is used: this value indicates the number of mathematical operations per second that the processor is able to perform.

The most modern hardware on the market use Teraflops or TFLOPS, a parameter which indicates 10¹² of floating point operations per second: GPU cards with high Teraflops power are among the most advanced on the market and allow for exceptional performance, suitable for managing massive amounts of data in the realm of AI.

In fact, the more FLOPS a GPU has, the greater its calculation capacity: computer systems equipped with Teraflops and high-speed VRAM memories can therefore achieve very high levels of performance and efficiency in parallel data processing, necessary for the functioning of applications Deep learning and machine vision AI.

CUDA: programming technology for AI

Among the most innovative GPU programming technologies we find NVIDIA's proposal for the management of AI applications: CUDA, acronym for Compute Unified Device Architecture. This system was a real revolution in the IT field as it allowed us to make the most of the potential of GPUs, which are already much more efficient than CPUs.

The CUDA framework provides developers with specific sets of parallel programming instructions, libraries of optimized features, and development tools to simplify the development of AI applications through a standardized interface.

NVIDIA CUDA GPUs can be programmed as several processors working in parallel, allowing you to reach very high computational power and create complex and efficient applications. Thanks to this, AI engineers have been able to make great progress in the field of artificial intelligence in recent years.

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Digimax Embedded PC with NVIDIA Jetson GPUs

As pioneers in the field of industrial automation, Digimax offers the most advanced hardware solutions for the development of software and applications with artificial intelligence. An example of these are embedded PCs with integrated NVIDIA Jetson model GPUs, designed for machine learning and computer vision systems.

Through partnerships with Advantech and AAEON, we can offer embedded PCs with leading NVIDIA Jetson GPU models for AI, such as:

• BOXER-8221-AI with Jetson Nano GPU: Entry-level computer with low cost and sufficient performance for basic AI applications;
• BOXER-8251AI with Jetson Xavier NX GPU: Most powerful embedded PC capable of supporting more complex AI algorithms;
• BOXER-8120AI with Jetson TX2 GPU: High-end device that offers outstanding performance for computer vision, speech recognition and advanced applications.
• MIC-730AI-10A1 with Jetson AGX Xavier GPU: most exclusive model with very high calculation capacity and performance, ideal for demanding and complex applications.

Industrial PCs with NVIDIA GPUs offered by Digimax support the most common programming tools such as Tensor Flow, PyTorch and Caffe 2. Thanks to their high efficiency they can also be used in environments with limited energy availability.

Ease of use, high flexibility and high performance: Digimax embedded PCs for AI can significantly accelerate the development of industrial automation applications, offering a complete and ready-to-use solution for complex data processing.

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