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  2. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    In January 2019, the TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. [30] In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be ...

  3. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  4. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    In computing, CUDA 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.

  5. Tensor Processing Unit - Wikipedia

    en.wikipedia.org/wiki/Tensor_Processing_Unit

    The SBC Coral Dev Board and Coral SoM both run Mendel Linux OS – a derivative of Debian. [ 45 ] [ 46 ] The USB, PCI-e, and M.2 products function as add-ons to existing computer systems, and support Debian-based Linux systems on x86-64 and ARM64 hosts (including Raspberry Pi ).

  6. General-purpose computing on graphics processing units

    en.wikipedia.org/wiki/General-purpose_computing...

    General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).

  7. 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]

  8. TensorFloat-32 - Wikipedia

    en.wikipedia.org/wiki/TensorFloat-32

    The binary format is: 1 sign bit; 8 exponent bits; 10 fraction bits (also called mantissa, or precision bits) The total 19 bits fits within a double word (32 bits), and while it lacks precision compared with a normal 32 bit IEEE 754 floating point number, provides much faster computation, up to 8 times on a A100 (compared to a V100 using FP32).

  9. Windows Subsystem for Linux - Wikipedia

    en.wikipedia.org/wiki/Windows_Subsystem_for_Linux

    Microsoft announced WSL 2 on May 6, 2019, [5] and it was shipped with Windows 10 version 2004. [14] It was also backported to Windows 10 version 1903 and 1909. [15] GPU support for WSL 2 to run GPU-accelerated machine learning was introduced in Windows build 20150. [16]