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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 ...
See also References External links A Accelerated Graphics Port (AGP) A dedicated video bus standard introduced by INTEL enabling 3D graphics capabilities; commonly present on an AGP slot on the motherboard. (Presently a historical expansion card standard, designed for attaching a video card to a computer's motherboard (and considered high-speed at launch, one of the last off-chip parallel ...
Google's TPU was developed in 2015 to accelerate DNN inference since the company projected that the use of voice search would require to double the computational resources allocated at the time for neural network inference. [13] The TPU was designed to be a co-processor communicating via a PCIe bus, to
A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and had found them to deliver an order of magnitude better ...
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).
Components of a GPU. A graphics processing unit (GPU) is a specialized electronic circuit initially designed for digital image processing and to accelerate computer graphics, being present either as a discrete video card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles.
A GPU cluster is a computer cluster in which each node is equipped with a graphics processing unit (GPU). By harnessing the computational power of modern GPUs via general-purpose computing on graphics processing units (GPGPU), very fast calculations can be performed with a GPU cluster. Titan, the first supercomputer to use GPUs
The CPU is responsible for processing input from the user and passing information to the GPU. The GPU does not have access to a computer's main memory so instead, graphical assets have to be loaded into VRAM which is the GPU's memory. The CPU is responsible for instructing the GPU while the GPU uses the information to render an image on to an ...