OpenVINO, short for Open Visual Inference and Neural Network Optimization, is an open-source toolkit developed by Intel for optimizing and deploying deep learning models on a variety of hardware platforms, including CPUs, GPUs, FPGAs (Field-Programmable Gate Arrays), and VPUs (Vision Processing Units). The primary goal of OpenVINO is to provide a unified framework that enables developers to accelerate and optimize their computer vision and deep learning applications for various Intel hardware platforms.

OpenVINO offers a range of tools and libraries that help streamline the deployment of neural networks for tasks like image recognition, object detection, and other computer vision tasks. It includes model optimization techniques to convert and adapt deep learning models to run efficiently on Intel hardware. This optimization is achieved through quantization, pruning, and other techniques to reduce the computational and memory requirements of models.

Key components of OpenVINO include the Model Optimizer for converting and optimizing models, the Inference Engine for running these models efficiently, and a set of pre-trained models for common computer vision tasks. It also supports popular deep learning frameworks such as TensorFlow, Caffe, and ONNX, making it easier for developers to bring their models into the OpenVINO framework.

OpenVINO is often used in applications such as video surveillance, robotics, autonomous vehicles, and more, where real-time, high-performance inference of deep learning models is required on Intel-based hardware platforms. It provides a bridge between deep learning research and practical deployment, especially for applications that require low latency and high throughput.