TPUs

Tensor Processing Units (TPUs) are application-specific integrated circuits (ASICs) designed by Google to accelerate machine learning workloads, particularly neural networks. They are built to deliver high throughput for large-scale tensor operations and are tightly integrated with Google's software stack, including TensorFlow and related tooling. TPUs are most commonly exposed as cloud accelerators through Google Cloud Platform as Cloud TPU devices.

Hardware and architecture summaries typically highlight a large array of matrix-multiply processing units, a design that favors dense linear algebra used in neural networks. TPUs emphasize high compute throughput and memory bandwidth, often employing low-precision numeric formats such as bfloat16 and int8 to improve performance and efficiency while maintaining model accuracy for many workloads. The devices are paired with on-chip or high-bandwidth off-chip memory and are connected via specialized interconnects to enable scalable performance across multiple chips in clusters known as TPU Pods.

Generations and usage have evolved from inference-focused accelerators to systems capable of training at scale. Early generations provided optimized inference, while later versions added training support and larger-scale deployment options. TPU Pods enable distributed training by connecting many TPU devices, enabling substantial acceleration for large models and extensive datasets. Access is primarily through Google Cloud, with tooling to manage data, model compilation, and distributed execution across devices.

Software ecosystem and workflow involve TensorFlow, XLA, and JAX, with compilers and runtimes that map neural network graphs to TPU hardware. Developers prepare data, compile models for the TPU, and run training or inference jobs in the Cloud TPU environment, often leveraging parallelism across multiple devices.

Edge TPU, a separate line, targets on-device inference in low-power environments and supports TensorFlow Lite for edge applications.