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  2. Deep Learning Super Sampling - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_super_sampling

    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. [38] The Tensor Cores use CUDA Warp -Level Primitives on 32 parallel threads to take advantage of their parallel architecture. [ 39 ]

  3. Volta (microarchitecture) - Wikipedia

    en.wikipedia.org/wiki/Volta_(microarchitecture)

    Tensor cores: A tensor core is a unit that multiplies two 4×4 FP16 matrices, and then adds a third FP16 or FP32 matrix to the result by using fused multiply–add operations, and obtains an FP32 result that could be optionally demoted to an FP16 result. [12] Tensor cores are intended to speed up the training of neural networks. [12]

  4. Neural processing unit - Wikipedia

    en.wikipedia.org/wiki/AI_accelerator

    A neural processing unit (NPU), also known as AI accelerator or deep learning processor, is a class of specialized hardware accelerator [1] or computer system [2] [3] designed to accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision.

  5. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    In computing, CUDA (Compute Unified Device Architecture) is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.

  6. Turing (microarchitecture) - Wikipedia

    en.wikipedia.org/wiki/Turing_(microarchitecture)

    The Tensor cores perform the result of deep learning to codify how to, for example, increase the resolution of images generated by a specific application or game. In the Tensor cores' primary usage, a problem to be solved is analyzed on a supercomputer, which is taught by example what results are desired, and the supercomputer determines a ...

  7. Pascal (microarchitecture) - Wikipedia

    en.wikipedia.org/wiki/Pascal_(microarchitecture)

    Painting of Blaise Pascal, eponym of architecture. Pascal is the codename for a GPU microarchitecture developed by Nvidia, as the successor to the Maxwell architecture. The architecture was first introduced in April 2016 with the release of the Tesla P100 (GP100) on April 5, 2016, and is primarily used in the GeForce 10 series, starting with the GeForce GTX 1080 and GTX 1070 (both using the ...

  8. NVDLA - Wikipedia

    en.wikipedia.org/wiki/NVDLA

    NVDLA is available for product development as part of Nvidia's Jetson Xavier NX, a small circuit board in a form factor about the size of a credit card which includes a 6-core ARMv8.2 64-bit CPU, an integrated 384-core Volta GPU with 48 Tensor Cores, and dual NVDLA "engines", as described in their own press release. [4]

  9. Nvidia NVENC - Wikipedia

    en.wikipedia.org/wiki/Nvidia_NVENC

    The Nvidia NVENC SIP core needs to be supported by the device driver. The driver provides one or more interfaces, (e.g. OpenMAX IL) to NVENC. The NVENC SIP core can be accessed through the proprietary NVENC API, as well as the DXVA and VDPAU [23] APIs. It is bundled with Nvidia's GeForce driver. NVENC is available for Windows and Linux ...