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CUDA 9.0–9.2 comes with these other components: CUTLASS 1.0 – custom linear algebra algorithms, NVIDIA Video Decoder was deprecated in CUDA 9.2; it is now available in NVIDIA Video Codec SDK; CUDA 10 comes with these other components: nvJPEG – Hybrid (CPU and GPU) JPEG processing; CUDA 11.0–11.8 comes with these other components: [20 ...
Tegra is a system on a chip (SoC) series developed by Nvidia for mobile devices such as smartphones, personal digital assistants, and mobile Internet devices. The Tegra integrates an ARM architecture central processing unit (CPU), graphics processing unit (GPU), northbridge , southbridge , and memory controller onto one package.
PyTorch is a machine learning library based on the Torch library, [4] [5] [6] used for applications such as computer vision and natural language processing, [7] originally developed by Meta AI and now part of the Linux Foundation umbrella.
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 ...
Torch is used by the Facebook AI Research Group, [8] IBM, [9] Yandex [10] and the Idiap Research Institute. [11] Torch has been extended for use on Android [12] [better source needed] and iOS. [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14]
WebGPU enables 3D graphics within an HTML canvas.It also has robust support for general-purpose GPU computations. [3]WebGPU uses its own shading language called WGSL that was designed to be trivially translatable to SPIR-V, until complaints caused redirection into a more traditional design, similar to other shading languages.
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A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]