A Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) built specifically to accelerate machine learning workloads. It is a hardware accelerator designed for artificial intelligence (AI) tasks, such as image recognition and language processing. The TPU helps machine learning algorithms run more efficiently by reducing the amount of time needed for neural network training and inference. This makes it possible to do machine learning faster, cheaper, and with better results than traditional computing methods. The TPU can be used on its own or in conjunction with other machine learning technologies for maximum performance. The flexible TPU architecture is designed to make machine learning algorithms run more efficiently and quickly, allowing for faster decision-making in a variety of scenarios. This makes it a powerful tool for machine learning and artificial intelligence applications.  

TPUs are designed to handle a high volume of low precision computation and have more input/output operations per joule than GPUs. Typically the TPU ASICs is a part of a data center solution as it can fit in the space of a hard drive slot within a data center rack. Different types of processors are suited for different types machine learning models, with TPUs being best for CNN’s, GPUs having benefits for some fully-connected neural networks, and CPUs providing advantages for RNN’s. 

TPUs are designed specifically for machine learning workloads and use Google’s custom-built tensor processing chips to achieve high levels of performance. The TPU is fast, efficient, and supports a number of ML frameworks such as TensorFlow and PyTorch. Additionally, the platform can be used across a variety of hardware architectures meaning it can easily scale to fit different needs. 

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