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{{cite web|url=https://www.tomshardware.com/news/nvidia-tensor-core-tesla-v100,34384.html|title=On Tensors, Tensorflow, And Nvidia's Latest 'Tensor Cores'|publisher=tomshardware.com|date=2017-04-11|access-date=2020-04-08}}</ref> They are used for doing [[Multiply–accumulate operation|fused multiply-add]] (FMA) operations that are used extensively in neural network calculations for applying a large series of multiplications on weights, followed by the addition of a bias. Tensor cores can operate on FP16, INT8, INT4, and INT1 data types. Each core can do 1024 bits of FMA operations per clock, so 1024 INT1, 256 INT4, 128 INT8, and 64 FP16 operations per clock per tensor core, and most Turing GPUs have a few hundred tensor cores.<ref>{{Cite web|title=Tensor Core DL Performance Guide|url=https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9926-tensor-core-performance-the-ultimate-guide.pdf|url-status=live|website=Nvidia|archive-url=https://web.archive.org/web/20201111223322/https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9926-tensor-core-performance-the-ultimate-guide.pdf |archive-date=2020-11-11 }}</ref> The Tensor Cores use [[CUDA]] [[Warp (CUDA)|Warp]]-Level Primitives on 32 parallel threads to take advantage of their parallel architecture.<ref>{{cite web|url=https://devblogs.nvidia.com/using-cuda-warp-level-primitives/|title=Using CUDA Warp-Level Primitives|publisher=[[Nvidia]]|date=2018-01-15|access-date=2020-04-08|quote=NVIDIA GPUs execute groups of threads known as warps in SIMT (Single Instruction, Multiple Thread) fashion.}}</ref> A Warp is a set of 32 [[Thread (computing)|threads]] which are configured to execute the same instruction. Since [[Windows 10 version 1903]], Microsoft Windows provided [[DirectML]] as one part of [[DirectX]] to support Tensor Cores.
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Particularly in early versions of DLSS, users reported blurry frames. Andrew Edelsten, an employee at Nvidia, therefore commented on the problem in a blog post in 2019 and promised that they were working on improving the technology and clarified that the DLSS AI algorithm was mainly trained with 4K image material. That the use of DLSS leads to particularly blurred images at lower resolutions, such as [[Full HD]], is due to the fact that the algorithm has far less image information available to calculate an appropriate image compared to higher resolutions like 4K.<ref>{{Cite web |title=NVIDIA DLSS: Your Questions, Answered |url=https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-your-questions-answered/ |access-date=2024-07-09 |publisher=Nvidia |language=en-us}}</ref>
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