Definition and purpose of NPUs

Summary

A neural processing unit (NPU), also called an AI accelerator or deep learning processor, is specialized hardware (or a computer system) designed to speed up artificial intelligence and machine learning workloads such as artificial neural networks and computer vision. An NPU may be standalone or integrated as part of a CPU or GPU. The purpose of an NPU is to execute AI tasks more efficiently—either by running already trained models (inference) such as large language models, or by supporting model training. Compared with general-purpose compute, NPUs can deliver better speed and/or lower power consumption, and they are commonly used in robotics, Internet of Things (IoT), and sensor-driven, data-intensive applications. Architecturally, NPUs often use manycore or spatial designs and emphasize low-precision arithmetic and specialized dataflow or in-memory computing approaches to improve performance.

Key Points

• 1

  NPUs are specialized hardware designed to accelerate AI and machine learning tasks like neural networks and computer vision.

• 2

  They are used for both inference (running trained models) and training, aiming for better speed and/or power efficiency.

• 3

  NPU designs often focus on low-precision operations and specialized architectures such as dataflow or in-memory computing.

• 4

  NPUs are deployed across consumer devices and datacenters, supporting applications in areas like robotics, IoT, and sensor-driven workloads.