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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 ...
CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most standard operating systems. CUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order): cuBLAS – CUDA Basic Linear Algebra Subroutines library; CUDART – CUDA Runtime library
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).
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.
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
The RK3028 is a low-cost dual-core ARM Cortex-A9-based processor clocked at 1.0 GHz with ARM Mali-400 GPU. It is pin-compatible with the RK2928. It is used in a few kids tablets and low-cost Android HDMI TV dongles. [21] The RK3026 is an updated ultra-low-end dual-core ARM Cortex-A9-based tablet processor clocked at 1.0 GHz with ARM Mali-400 ...
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.
In August 2018 Intel acquired Vertex.AI, a startup whose mission statement was “deep learning for every platform”. [6] Intel released PlaidML as free software under to the terms of the Apache Licence (version 2.0) to improve compatibility with nGraph, TensorFlow, and other ecosystem software.