👉 VG (Vision-GPU) computing is an architectural approach that integrates vision processing units (VPUs) with traditional graphics processing units (GPUs) to enhance the performance and efficiency of deep learning models, particularly those focused on computer vision tasks. By offloading specific vision-related computations to the VPUs, VG computing reduces the computational burden on the main CPU/GPU, allowing for faster inference and training of neural networks. This integration leverages the parallel processing capabilities of GPUs, which are well-suited for matrix operations common in deep learning, while the VPUs excel at handling the specialized, high-throughput tasks of image and video analysis. As a result, VG computing enables more efficient deployment of AI models in real-time applications, such as autonomous driving, augmented reality, and medical imaging, where speed and accuracy are critical.
