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  2. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

  3. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow includes an “eager execution” mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. [35] Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. [35]

  4. AMD Instinct - Wikipedia

    en.wikipedia.org/wiki/AMD_Instinct

    AMD Instinct is AMD's brand of data center GPUs. [1] [2] It replaced AMD's FirePro S brand in 2016.Compared to the Radeon brand of mainstream consumer/gamer products, the Instinct product line is intended to accelerate deep learning, artificial neural network, and high-performance computing/GPGPU applications.

  5. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms.

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices.

  7. Tensor Processing Unit - Wikipedia

    en.wikipedia.org/wiki/Tensor_Processing_Unit

    In January 2019, Google made the Edge TPU available to developers with a line of products under the Coral brand. The Edge TPU is capable of 4 trillion operations per second with 2 W of electrical power. [44] The product offerings include a single-board computer (SBC), a system on module (SoM), a USB accessory, a mini PCI-e card, and an M.2 card.

  8. Nvidia Jetson - Wikipedia

    en.wikipedia.org/wiki/Nvidia_Jetson

    384-core Nvidia Volta architecture GPU with 48 Tensor cores 6-core Nvidia Carmel ARMv8.2 64-bit CPU 6MB L2 + 4MB L3 8 GiB 10–20W 2023 Jetson Orin Nano [20] 20–40 TOPS from 512-core Nvidia Ampere architecture GPU with 16 Tensor cores 6-core ARM Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 4–8 GiB 7–10 W 2023 Jetson Orin NX 70–100 TOPS

  9. NetBIOS over TCP/IP - Wikipedia

    en.wikipedia.org/wiki/NetBIOS_over_TCP/IP

    In addition, to start a session or to send a datagram to a particular host rather than to broadcast the datagram, NBT will have to determine the IP address of the host with a given NetBIOS name; this is done by broadcasting a "Name Query" packet, and/or sending it to the NetBIOS name server. The response will have the IP address of the host ...

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