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Artificial Intelligence (AI) is becoming increasingly prevalent in today’s technology world. It has impacted many different industries, and one of the most prominent areas it is making strides in is the field of graphics. Graphics cards with AI capabilities are changing the way we think about computer graphics, and tensor cores have emerged as the latest technology to help power AI performance in graphics cards.
But what are tensor cores and how do they work?
Tensor Cores: An Introduction
Tensor cores are a new type of processing unit that are specifically designed for artificial intelligence workloads. They are available in the newest generations of Nvidia graphics cards starting with the GeForce RTX 20-series and are built on a new architecture called Turing.
There are essentially two types of processors in most graphics cards: rasterization processors and compute processors. Rasterization processors are in charge of making sure the graphics card renders images correctly, while compute processors deal with tasks that require computation such as physics simulations, video encoding, and other tasks. Tensor cores are compute processors that are specifically designed to accelerate AI workloads such as deep learning, neural networks, and machine learning.
Tensor cores use a mixed-precision format (which is a combination of 16-bit and 32-bit floating point calculations). This allows tensor cores to perform matrix operations at extremely high speeds, making them well-suited to the computations involved in deep learning.
And as AI becomes more complex and requires more complex calculations, tensor cores are helping to take these computations off the shoulders of traditional compute processors and speeding up AI performance significantly.
How Do Tensor Cores Improve AI Performance?
AI workloads are extremely compute-intensive and require a lot of processing power. In graphics cards, this means the workloads are typically split between the rasterization and compute processors. Tensor cores are specifically designed to handle these AI workloads, taking the load off of the rasterization and compute processors, and making the calculations faster and more efficient.
Tensor cores work by processing the data in blocks, known as “tiles.” They can process up to 4 tiles simultaneously, which means they are particularly well-suited to deep learning computations such as matrix multiplication, convolutional neural networks, and other tasks involved in training AI models.
Some other ways that tensor cores can improve AI performance in graphics cards include:
1. Efficient Matrix Multiplication
Matrix multiplication is one of the most time-consuming tasks involved in training AI models. Tensor cores are specifically designed for this type of operation, allowing them to perform matrix multiplication faster and more efficiently than traditional compute processors. This means that models can be trained faster, which can lead to better AI results in less time.
2. Faster Inference
Inference refers to the process of using a trained AI model to make predictions based on new data. Tensor cores can speed up this process significantly, as they are designed to perform the calculations required for inference quickly and efficiently. This can lead to faster and more accurate predictions from AI models.
3. Better Image and Video Processing
Tensor cores are also well-suited to tasks such as image and video processing, as they can handle multiple tasks simultaneously. This can lead to better image and video quality, as well as faster processing times.
Overall, tensor cores are a significant advancement in the world of AI and graphics. They allow for faster and more efficient AI workloads, which can lead to better results in less time. And as AI continues to become more prevalent in our daily lives, tensor cores will play an increasingly important role in powering the AI capabilities of our graphics cards.
Conclusion
In conclusion, tensor cores are an exciting development in the world of AI and graphics. They are specifically designed to accelerate AI workloads and can perform matrix multiplication, convolutional neural networks, and other tasks involved in deep learning and neural networks much faster and more efficiently than traditional compute processors.
As AI becomes more prevalent in our daily lives, the power of tensor cores in graphics cards will become more and more important. They will enable us to train and run more complex AI models, making our technology more powerful and more useful in a wide range of industries.
So if you’re looking for a graphics card with top-of-the-line AI capabilities, look for one with tensor cores. They will make your AI workloads faster, more efficient, and more powerful than ever before.
Image Credit: Pexels