A Tensor Processing Unit (TPU) is a specialized hardware accelerator developed by Google for accelerating machine learning workloads, particularly those involving deep learning and neural networks. TPUs are designed to perform matrix computations efficiently, which are at the core of many machine learning algorithms. They are optimized to handle the types of operations that are commonly found in neural network training and inference, making them highly efficient for AI tasks.
TPUs offer impressive speed and power efficiency compared to traditional central processing units (CPUs) and graphics processing units (GPUs) for certain AI workloads. This is achieved by customizing the hardware architecture specifically for the computational needs of neural networks. TPUs are well-suited for large-scale training tasks and can significantly reduce the time required to train complex models. They’re particularly advantageous in cloud-based AI services, enabling faster model development and deployment.
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