Intel Movidius

Intel Movidius refers to Intel’s family of low-power vision processing units (VPUs): chips built to run computer-vision and neural-network workloads close to a camera or sensor. Instead of sending every video frame to a cloud server, a Movidius chip helps the device understand what it sees locally.

What the hardware does
A Movidius VPU, such as the Myriad X, combines programmable processing cores with dedicated hardware for image and neural-network operations. It is designed for tasks including image resizing, camera processing, feature extraction, and inference—the stage where a trained model turns pixels into results. The chip handles the repeated matrix and convolution calculations used by vision models far more efficiently than a general-purpose CPU running alone. Intel packaged this technology in products such as the Neural Compute Stick, a USB accelerator that could add local AI inference to a small computer.

How it is used
Developers typically convert a trained model into a format optimized for the VPU, then deploy it through Intel’s OpenVINO toolkit. A practical setup might look like this:

• A smart camera captures a frame.
• OpenVINO sends an object-detection model to the Movidius VPU.
• The VPU returns labels, locations, or counts—for example, “person,” “forklift,” or “empty parking space.”
• The host computer acts only on those compact results rather than transmitting raw video.

Why it matters on devices
Vision workloads can overwhelm small CPUs, drain batteries, and create heat when they run continuously. A Movidius accelerator shifts that work to hardware tuned for efficient inference, enabling responsive offline systems such as industrial inspection cameras, robots, and access-control devices. Its limits still shape deployment: the model must fit supported operations and available memory, and developers must measure real latency, power draw, and thermals rather than assuming a desktop-sized model will run well on a compact device.