NPU

Neural Processing Unit

A specialized microprocessor designed to accelerate the execution of artificial intelligence (AI) workloads, particularly those involving neural network computations. Unlike general-purpose CPUs or even GPUs, NPUs are specifically designed to handle the parallel, matrix-intensive operations required for deep learning inference and training tasks.

NPUs excel at operations such as tensor multiplication, convolution, and activation functions, which are foundational to modern neural networks. By offloading these tasks from the CPU or GPU, NPUs significantly improve the efficiency, speed, and energy consumption of AI applications. This makes them especially valuable in scenarios requiring on-device intelligence, such as mobile phones, smart cameras, autonomous vehicles, IoT devices, and edge computing systems.

Modern NPUs typically support multiple AI frameworks (like TensorFlow Lite, PyTorch Mobile, or ONNX) and include features such as dedicated memory bandwidth, low-precision arithmetic (e.g., INT8 or FP16), and parallel processing cores. Some NPUs are integrated into system-on-chip (SoC) designs alongside CPUs and GPUs, enabling seamless hybrid processing for complex tasks that combine AI with traditional computation or graphics.

As AI adoption grows across industries, NPUs are increasingly recognized as a cornerstone technology for deploying machine learning models at scale with optimal performance, particularly in environments where latency, power efficiency, and local processing are essential.