In this blog post we show you how you to use Tensor Cores in your own application using CUDA Libraries as well as how to program them directly in CUDA C++ device code. For more information about enabling Tensor Cores when using these frameworks, check out the Mixed-Precision Training Guide. For Deep Learning inference the recent TensorRT 3 release also supports Tensor Cores. Tensor Cores are already supported for Deep Learning training either in a main release or via pull requests in many Deep Learning frameworks (including Tensorflow, PyTorch, MXNet, and Caffe2). Tensor Cores enable AI programmers to use mixed-precision to achieve higher throughput without sacrificing accuracy. Tensor cores are programmable using NVIDIA libraries and directly in CUDA C++ code.Ī defining feature of the new Volta GPU Architecture is its Tensor Cores, which give the Tesla V100 accelerator a peak throughput 12 times the 32-bit floating point throughput of the previous-generation Tesla P100. Tensor cores provide a huge boost to convolutions and matrix operations.
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