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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 ...
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).
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
Hardware acceleration is the use of computer hardware designed to perform specific functions more efficiently when compared to software running on a general-purpose central processing unit (CPU). Any transformation of data that can be calculated in software running on a generic CPU can also be calculated in custom-made hardware, or in some mix ...
The CPU first detects OpenCL devices (GPU in this case) and then invokes a just-in-time compiler to translate the OpenCL source code into target binary. CPU then sends data to GPU to perform computations. When the GPU is processing data, CPU is free to process its own tasks.
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.
In other words, the CPU(s) run (possibly in parallel), then when that subtask completes, the next subtask is handed to the GPU(s). When they finish the next subtask is handed to the TPU(s). [2] Adding software pipelining allows the second subtask to run using partial results from the first subtask, which improves resource utilization. [2]
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 ...