NPU

NPU stands for Neural Processing Unit, a specialized microprocessor designed to accelerate artificial neural networks. NPUs target the core computations of deep learning models, such as large matrix multiplications, convolutions, and nonlinear activations, with architectures and data flows optimized for low latency and high energy efficiency. They are used primarily for inference, enabling AI features in devices from smartphones to data-center servers; some designs also support limited on-device training or fine-tuning.

Architectures of NPUs vary, but common features include dedicated compute engines, on-chip memory, and specialized interconnects to improve data locality. They often employ quantized arithmetic (for example int8, int16, bf16, or float16) and may support mixed precision. Designs may use tensor processors, systolic arrays, or vector units, and rely on a neural network compiler to map models to hardware. A key emphasis is on memory bandwidth and data reuse, with on-chip caches or scratchpad memories to minimize off-chip traffic. Software stacks typically include libraries and toolchains that support common frameworks and model formats, enabling conversion from TensorFlow, PyTorch, or ONNX to hardware-specific kernels.

Deployment and usage of NPUs span mobile devices, embedded systems, automotive applications, and data-center accelerators. They contribute to lower power consumption and higher inference throughput, supporting real-time tasks such as image and video processing, voice recognition, and object detection. Notable families and implementations include Arm’s Ethos-N and Ethos-U lines, Huawei’s Ascend NPUs, and various vendor-specific NPUs integrated into mobile SoCs and server accelerators. NPUs are often discussed alongside GPUs and CPUs as specialized AI accelerators.