Nvidia open-source project speeds computer vision cloud apps

Market news |
By Rich Pell

AI computing technology company Nvidia has introduced an open-source library for building accelerated end-to-end computer vision and image processing pipelines. CV-CUDA aims to address the increasing use of internet video data that will be augmented by AI special effects and computer graphics.

Fast-growing social media and video-sharing services are experiencing growing cloud computing costs and bottlenecks in their AI-based imaging processing and computer vision pipelines, says the company. CV-CUDA accelerates AI special effects such as relighting, reposing, blurring backgrounds and super resolution.

The company’s GPUs already accelerate the inference portion of AI computer vision pipelines. But pre- and post-processing using traditional computer vision tools gobble up time and computing power.

CV-CUDA gives developers more than 50 high-performance computer vision algorithms, a development framework that makes it easy to implement custom kernels and zero-copy interfaces to remove bottlenecks in the AI pipeline. The result is higher throughput and lower cloud-computing costs. CV-CUDA can process 10x as many streams on a single GPU.

All this, says the company, helps developers move much faster when tackling video content creation, 3D worlds, image-based recommender systems, image recognition and video conferencing. Video content creation platforms must process, enhance and moderate millions of video streams daily and ensure mobile-based users have the best experience running their apps on any phone:

  • For those building 3D worlds or metaverse applications, CV-CUDA is anticipated to enable tasks to help build or extend 3D worlds and their components.
  • In image understanding and recognition, CV-CUDA can significantly speed up the pipelines running at hyperscale, allowing mobile users to enjoy sophisticated and responsive image recognition applications.
  • And in video conferencing, CV-CUDA can support sophisticated augmented reality-based features. These features could involve complex AI pipelines requiring numerous pre- and post-processing steps.

CV-CUDA accelerates pre- and post-processing pipelines through hand-optimized CUDA kernels and natively integrates into C/C++, Python and common deep learning frameworks, such as PyTorch. CV-CUDA will be one of the core technologies that can accelerate AI workflows in NVIDIA Omniverse, a virtual world simulation and collaboration platform for 3D workflows.

Developers will be able to get early access to code in December, with a beta release set for March.



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