Why

• OpenVINO now supports inference on integrated NPU devices in intel's Core Ultra series processors.
• Sometimes as fast as GPU, but should use considerably less power.

How

• The NPU plugin is now available as "NPU" in openvino ov::Core::get_available_devices().
• Removed the guards and checks for NPU in available targets for Inference Engine backend.

Test example

Pre-requisites

• Intel Core Ultra series processor
• Intel NPU driver
• OpenVINO 2023.3.0+ (Tested on 2025.1.0)

Example

#include <opencv2/dnn.hpp>
#include <iostream>

int main(){
    cv::dnn::Net net = cv::dnn::readNet("../yolov8s-openvino/yolov8s.xml", "../yolov8s-openvino/yolov8s.bin");
    cv::Size net_input_shape = cv::Size(640, 480);
    std::cout << "Setting backend to DNN_BACKEND_INFERENCE_ENGINE and target to DNN_TARGET_NPU" << std::endl;
    net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
    net.setPreferableTarget(cv::dnn::DNN_TARGET_NPU);

    cv::Mat image(net_input_shape, CV_8UC3);
    cv::randu(image, cv::Scalar(0, 0, 0), cv::Scalar(255, 255, 255));
    cv::Mat blob = cv::dnn::blobFromImage(
        image, 1, net_input_shape, cv::Scalar(0, 0, 0), true, false, CV_32F);
    net.setInput(blob);
    std::cout << "Running forward" << std::endl;
    cv::Mat result = net.forward();
    std::cout << "Output shape: " << result.size << std::endl; // Output shape: 1 x 84 x 6300
}

model files here

docker image used to build opencv: ghcr.io/mro47/opencv-builder

Closes #26240

Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

• I agree to contribute to the project under Apache 2 License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
• The PR is proposed to the proper branch
• There is a reference to the original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake