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TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [ citation needed ] Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs ), and from desktops to clusters of servers to mobile and edge devices .
PlaidML is a portable tensor compiler.Tensor compilers bridge the gap between the universal mathematical descriptions of deep learning operations, such as convolution, and the platform and chip-specific code needed to perform those operations with good performance.
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]
PyTorch has also been developing support for other GPU platforms, for example, AMD's ROCm [27] and Apple's Metal Framework. [28] PyTorch supports various sub-types of Tensors. [29] Note that the term "tensor" here does not carry the same meaning as tensor in mathematics or physics.
Rockchip announced the first member of the RK33xx family at the CES show in January 2015. The RK3368 is a SoC targeting tablets and media boxes featuring a 64-bit octa-core Cortex-A53 CPU and an OpenGL ES 3.1-class GPU. [40] Octa-Core Cortex-A53 64-bit CPU, up to 1.5 GHz; PowerVR SGX6110 GPU with support for OpenGL 3.1 and OpenGL ES 3.0; 28 nm ...
This has implications for correctness which are considered important to some scientific applications. While 64-bit floating point values (double precision float) are commonly available on CPUs, these are not universally supported on GPUs. Some GPU architectures sacrifice IEEE compliance, while others lack double-precision.
The latest releases also have packages that allow for easy setup for TensorFlow and CUDA. [5] [6] Pop!_OS is maintained primarily by System76, with the release version source code hosted in a GitHub repository. Unlike many other Linux distributions, it is not community-driven, although outside programmers can contribute, view and modify the ...
MLIR (Multi-Level Intermediate Representation) is a unifying software framework for compiler development. [1] MLIR can make optimal use of a variety of computing platforms such as central processing units (CPUs), graphics processing units (GPUs), data processing units (DPUs), Tensor Processing Units (TPUs), field-programmable gate arrays (FPGAs), artificial intelligence (AI) application ...