TPU (Tensor Processing Unit) is a specialized processor that is designed to accelerate machine learning workloads, particularly those used in neural networks. TPUs are similar to GPUs, but they are specifically designed to handle the specific requirements of deep learning workloads, making them more powerful and efficient.
In AI, TPUs are commonly used for training and inference of deep neural networks. They are optimized for performing large number of matrix multiplications and other mathematical operations that are required for deep learning. TPUs can perform these operations faster and more efficiently than general-purpose CPUs and GPUs.
TPUs also have specialized memory and interconnects optimized for deep learning workloads, making them more efficient at handling large amounts of data and improving performance. Additionally, TPUs can also be used to perform other machine learning workloads such as reinforcement learning and computer vision.
In summary, TPUs are specialized processors that are designed to accelerate machine learning workloads, particularly those used in neural networks. They are more powerful and efficient than general-purpose CPUs and GPUs and can be used to speed up both training and inference processes in deep neural networks and other machine learning workloads.
