Tensor Processing Unit
Tensor Processing Units (TPUs) are custom application-specific integrated circuits (ASICs) developed by Google specifically for accelerating machine learning workloads, particularly those involving tensor operations common in neural networks. They are optimized for high-throughput, low-precision matrix computations and are deployed in Google Cloud Platform to provide scalable AI/ML infrastructure. TPUs offer significant performance and energy efficiency advantages over general-purpose CPUs and GPUs for training and inference tasks in deep learning.
Developers should learn about TPUs when working on large-scale machine learning projects that require fast training of complex models, such as natural language processing, computer vision, or recommendation systems, especially if using TensorFlow or JAX frameworks. They are particularly valuable in production environments where cost-efficiency and low-latency inference are critical, such as in cloud-based AI services or research requiring extensive computational resources. Using TPUs can drastically reduce training times and operational costs compared to traditional hardware.