We are thrilled to introduce you the TIM-VX backend integrated in OpenCV DNN, which allows OpenCV DNN runs quantized DL models in neural processing units (NPU) on edge devices, such as Khadas VIM3 etc. It achives up to 2X faster than ARM CPU backend for running face detection and recognition model from OpenCV Zoo. More details can be found in OpenCV Zoo Benchmarks.
TIM-VX is provided with x86_64 simulator. So you can try OpenCV with TIM-VX backend on your x86_64 machine following steps below, or if you happen to have a physical board equiped with the A311D chip (like the Khadas VIM3 mentioned above). In this guide, we provide two ways compiling OpenCV with TIM-VX backend:
- (Recommanded) Compile OpenCV together with TIM-VX.
- Compile OpenCV with TIM-VX library installed previously.