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  2. Deep Learning Super Sampling - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_super_sampling

    Nvidia advertised DLSS as a key feature of the GeForce 20 series cards when they launched in September 2018. [4] At that time, the results were limited to a few video games, namely Battlefield V, [5] or Metro Exodus, because the algorithm had to be trained specifically on each game on which it was applied and the results were usually not as good as simple resolution upscaling.

  3. 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.

  4. NVDLA - Wikipedia

    en.wikipedia.org/wiki/NVDLA

    The NVIDIA Deep Learning Accelerator (NVDLA) is an open-source hardware neural network AI accelerator created by Nvidia. [1] The accelerator is written in Verilog and is configurable and scalable to meet many different architecture needs. NVDLA is merely an accelerator and any process must be scheduled and arbitered by an outside entity such as ...

  5. NVIDIA (NVDA) Gaming Drives the Deep Learning-AI Revolution

    www.aol.com/news/nvidia-nvda-gaming-drives-deep...

    NVIDIA (NASDAQ:NVDA) is not only reinventing industries but actually creating new industries with its GPU-based deep learning and artificial intelligence technologies. Source: via Nvidia Today’s ...

  6. Turing (microarchitecture) - Wikipedia

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

    Turing is the codename for a graphics processing unit (GPU) microarchitecture developed by Nvidia. It is named after the prominent mathematician and computer scientist Alan Turing . The architecture was first introduced in August 2018 at SIGGRAPH 2018 in the workstation-oriented Quadro RTX cards, [ 2 ] and one week later at Gamescom in consumer ...

  7. Nvidia - Wikipedia

    en.wikipedia.org/wiki/Nvidia

    In April 2016, Nvidia produced the DGX-1 based on an 8 GPU cluster, to improve the ability of users to use deep learning by combining GPUs with integrated deep learning software. [190] Nvidia gifted its first DGX-1 to OpenAI in August 2016 to help it train larger and more complex AI models with the capability of reducing processing time from ...

  8. Parallel Thread Execution - Wikipedia

    en.wikipedia.org/wiki/Parallel_Thread_Execution

    The Nvidia CUDA Compiler (NVCC) translates code written in CUDA, a C++-like language, into PTX instructions (an IL), and the graphics driver contains a compiler which translates PTX instructions into executable binary code, [2] which can run on the processing cores of Nvidia graphics processing units (GPUs).

  9. DeepSpeed - Wikipedia

    en.wikipedia.org/wiki/DeepSpeed

    Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5] The team claimed to achieve up to a 6.2x throughput improvement, 2.8x faster convergence, and 4.6x less communication. [6]