How do Tensor Cores in Graphics Cards Unlock AI Superpowers?
Technology is constantly evolving, and the advancements we’ve seen in Artificial Intelligence (AI) recently wouldn’t be possible without Tensor Cores in Graphics Cards. Tensor Cores have the power to unlock massive computational abilities, which has led to an increase in AI-related technologies. In this article, we’ll discuss what Tensor Cores are, how they work, and how they help unlock AI superpowers.
What are Tensor Cores?
Tensor Cores are specialized processing units designed to perform mathematical operations. They were first introduced by NVIDIA in 2017 and are present in NVIDIA’s Volta, Turing and Ampere GPUs.
Now you might be wondering: what is the role of Tensor Cores in AI? Tensor Cores are designed to perform specific types of arithmetic operations, which are commonly used in AI and Deep Learning (DL).
In traditional processing units, when complex operations are performed, every calculation is done step-by-step, leading to lengthy calculations even for smaller operations. However, Tensor Cores were designed to perform these complex mathematical operations in parallel, which allows for faster processing of larger datasets, leading to more accurate AI predictions.
How do Tensor Cores Work?
Here’s the technical part of what Tensor Cores do. If you’re not tech-savvy, feel free to skip ahead.
In brief, Tensor Cores work by performing Tensor Operations – which are prevalent in AI and DL. Tensor Operations are essentially multi-dimensional matrix or vector equations developed to identify patterns within data.
Tensor Cores are built with hardware that delivers parallel multiplication and accumulation, allowing for faster performance of tensor operations than conventional processors.
Tensor Cores perform mixed-precision or low-precision calculations, which allows them to handle the more complex operations needed in Artificial Neural Networks (ANN). Additionally, they calculate complex matrix operations with reduced precision, which allows the system to deliver more accurate results, faster.
How Tensor Cores Unlock AI Superpowers?
Now that we know what Tensor Cores are and how they function, let’s discuss how they unlock AI superpowers.
Tensor Cores offer significant performance acceleration by allowing the execution of AI algorithms at lightning-fast speeds. By reducing the amount of time it takes to carry out complex calculations in AI models, Tensor Cores assist in improving their accuracy and efficiency. Tensor Cores further unlock the potential of Deep Learning, which is a subset of Artificial Intelligence.
Tensor Cores in NVIDIA GPUs, provide a powerful way to train large Deep Learning models, including image and natural language models.
In the earliest stages of AI, when it was based on simple decision trees, the training of algorithms on datasets was quick and easy. However, as AI systems advanced and required more complex patterns recognition for decision-making, the size of the training datasets exploded.
As a result, training even a simple neural network became a challenge with Backpropagation Algorithm that required extensive calculations. Tensor Cores speeds up the Backpropagation Algorithm, thereby making Deep Neural Networks more accessible and easier to train.
In addition to speeding up the Backpropagation Algorithm, Tensor Cores can also process large data sets available in the field of AI and Machine Learning. Research and development require the use of massive data sets, and Tensor Cores bring the primary advantage of reducing the time required for AI experiments to deliver results.
Tensor Cores also offer significant benefits in fields like natural language processing (NLP) and computer vision (CV).
Tensor Cores and Natural Language Processing (NLP)
Language processing is an essential part of NLP. The key is to translate natural human language into something that machines can understand. Traditional Language Processing was a rule-based approach that failed to determine most of the complexities of human-written or spoken language.
However, Deep Learning models using RNN (Recurrent Neural Networks) and Transformer-based architectures have shown great promise in dealing with the complexity of natural languages. These models require a large number of computations, which are executed quickly and efficiently by Tensor Cores.
Tensor Cores assist in speeding up the training of large language models, making them more accurate and efficient under different conditions.
Tensor Cores and Computer Vision (CV)
Computer Vision is one of the most critical subsets of AI, providing advanced functionality to areas like robotics and self-driving cars. Most of the recent advancements in computer vision are due to DL models. DL models require a large amount of data, making the training process time-intensive.
Tensor Cores speed up the training process of computer vision applications, allowing for a faster processing of data than the standard central processing unit (CPU), offering improved accuracy and giving computer vision models powerful power-scalable performance that is required to build devices such as real-time object recognition on CCTV cameras and self-driving cars.
Final Thoughts
Tensor Cores are essential components of graphics cards that unlock AI superpowers that were not possible previously. They accelerate the processing of AI algorithms by allowing computations to occur in parallel simultaneously. By drastically reducing the time it takes for DL models to train, not only are these systems are smarter and more flexible, but their integration into modern computing systems is only increasing with time.
We hope this article has been informative in explaining the role of Tensor Cores in unlocking AI superpowers. Feel free to learn more about how this technology is changing industries, including healthcare, manufacturing, and retail. Do you have any questions about Tensor Cores or AI? Let us know in the comments.
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