AiVibeNeural Processing Unit (NPU)
A neural processing unit (NPU) is a processor or processor block designed specifically to accelerate neural network inference, particularly low-precision matrix operations, with high efficiency per watt in phones, laptops and edge devices.
NPUs use arrays of multiply-accumulate units, on-chip memory and dataflow designs optimised for operations such as convolutions and matrix multiplications, usually at INT8 or lower precision. They appear as blocks inside smartphone and PC processors, as standalone accelerators and in embedded systems-on-chip. Google's Tensor Processing Units are a related class of accelerator used in data centres. Models are compiled for an NPU with vendor toolkits, such as OpenVINO for Intel NPUs.
NPUs run on-device features such as camera image enhancement, speech recognition and background blur on phones and laptops, and in industry they power smart cameras, vision sensors and edge gateways where power budgets are tight and cooling is limited.
Not every model operation is supported on every NPU, so unsupported layers may fall back to the CPU or GPU, reducing performance. Peak TOPS figures rarely reflect real application throughput, so models should be benchmarked on the target device. Quantisation is usually required to use an NPU effectively.
Key points
- A dedicated accelerator for neural network inference
- Optimised for low-precision matrix and convolution operations
- Found in phones, laptops, smart cameras and edge devices
- Benchmark real models, since peak TOPS can mislead
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.