Google Tensor Processing Unit (TPU) is a custom-designed application-specific integrated circuit (ASIC) developed by Google for accelerating machine learning (ML) workloads. TPUs are designed to handle the computationally intensive tasks involved in ML training and inference, such as matrix multiplication and convolution operations.
TPUs offer several advantages over traditional CPUs and GPUs for ML workloads. They are designed specifically for ML tasks, which allows them to achieve higher performance and efficiency. TPUs also have a lower cost per unit of compute than CPUs and GPUs, making them a more cost-effective option for large-scale ML deployments.