NVIDIA Tensor Cores
Tensor Cores are specialized processing units designed by NVIDIA to accelerate deep learning and AI workloads. First introduced in 2017 with the Volta architecture and the NVIDIA Tesla V100, Tensor Cores were developed to handle matrix multiplication operations central to machine learning algorithms. By performing these calculations at much higher speeds than traditional GPU cores, Tensor Cores significantly improve the performance of applications requiring large-scale matrix computations, such as neural network training and inference. https://en.wikipedia.org/wiki/Tensor_core
In addition to AI and machine learning, Tensor Cores power innovative gaming features like Deep Learning Super Sampling (DLSS). Integrated into consumer GPUs starting with the Turing architecture in 2018, Tensor Cores enable real-time AI-powered image enhancement and resolution upscaling. These advancements allow GPUs to deliver higher frame rates and improved visuals, making gaming at 4K and higher resolutions more accessible. Tensor Cores also accelerate professional workflows in AI, video editing, and data science by optimizing tasks like object detection and natural language processing. https://www.nvidia.com/en-us/data-center/tesla-v100/
With each GPU generation, Tensor Cores have evolved, increasing performance and efficiency. In the Ampere architecture, introduced in 2020, third-generation Tensor Cores added support for sparsity, which reduces computation time by focusing on significant data while skipping less relevant information. This innovation doubled the efficiency of AI computations. Tensor Cores continue to play a pivotal role in modern GPUs, bridging the gap between high-performance gaming and professional AI applications. https://www.nvidia.com/en-us/technologies/tensor-cores/
Tensor Cores are also instrumental in the field of high-performance computing (HPC), where they accelerate simulations, predictive modeling, and scientific computations. Their ability to handle mixed-precision computations, combining FP16 and FP32 formats, ensures both speed and accuracy in intensive workloads. This capability has made Tensor Cores a critical component for researchers and organizations utilizing supercomputing for breakthroughs in healthcare, climate science, and physics. By streamlining AI and computational tasks, Tensor Cores continue to expand the potential of GPUs across industries.
https://www.nvidia.com/en-us/research/ai/
Tensor Cores' integration into NVIDIA’s RTX GPUs and data center solutions has solidified their importance in the evolving landscape of AI and gaming. As deep learning and real-time ray tracing become standard features in both consumer and professional applications, Tensor Cores ensure that GPUs remain at the forefront of these advancements. Their role in accelerating AI workloads and enhancing graphics performance underscores their versatility and necessity in modern computing.