Khronos Blog

Announcements, articles, and blurbs from Khronos and Khronos members about Khronos tech, conformant products, and more. If you are a interested in submitting a blog post, please check out our Blog Guidlines.


In April, Khronos introduced the Safety Critical Advisory Forum was created in response to developers’ growing concerns and demands of functional safety standards on hardware and software. The advice and support that the forum provides to Khronos Working Groups directly contributes to the creation of SC APIs. Members and non-members can contribute in the forum, this post outlines the benefits of participation.

Facebook’s recent adoption of glTF 2.0 enables its users to place and see 3D content in their News Feeds, underscoring the social media platform’s plan to enable users to bring 3D objects and assets with them across AR, VR, mobile, and web experiences — using open standards. Facebook’s prominent support for glTF is already stimulating the creation of innovative tools to generate glTF content, such as Sony 3D Creator, Oculus Medium, and Foundry Modo.

As technology around artificial intelligence and autonomous driving advances, the need for safety critical systems also grows. Khronos Group has created a Safety Critical Advisory Forum and invites functional safety experts to join the group to advise and help develop open standards for the safety critical domain. By contributing your expertise to Khronos’ safety-critical work, you will enable Khronos and its API designers to deliver safety-critical APIs for a safer autonomous world.

Subgroups are an important new feature in Vulkan 1.1 because they enable highly-efficient sharing and manipulation of data between multiple tasks running in parallel on a GPU. In this tutorial, we will cover how to use the new subgroup functionality.

Standards make life easier, and we depend on them for more than we might realize — from knowing exactly how to drive any car, to knowing how to get hot or cold water from a faucet. When they fail us, the outcome can be comical or disastrous: non-standard plumbing, for instance, can result in an unexpected cold shower or a nasty scald. We need standards, and the entire computing world is built on them.

NNEF and ONNX are two similar open formats to represent and interchange neural networks among deep learning frameworks and inference engines. At the core, both formats are based on a collection of often used operations from which networks can be built. Because of the similar goals of ONNX and NNEF, we often get asked for insights into what the differences are between the two. Although Khronos has not been involved in the detailed design principles of ONNX, in this post we explain how we see the differences according to our understanding of the two projects. We welcome constructive discussion as the industry explores the need for neural network exchange and hope this post may be a constructive start to that conversation.

Authoring content for a new file format can be exciting, liberating, and at the same time scary. To be the most efficient and avoid frustration, it helps to understand the format's requirements. To help achieve that, I am going to walk through several paths for authoring content in the glTF format as well as outline specific settings to maximize your success. I will touch on both free and commercial software packages to ensure everyone has a path into glTF, but first let's outline a few important concepts.

Every year in December, millions of people get in the holiday spirit with NORAD Tracks Santa, the website that lets you track Santa’s magical midnight voyage through the sky on Christmas Eve. Part of what makes the NORAD Tracks Santa website possible are Khronos standards WebGL and glTF. Today, over 22 million people follow Santa’s journey on a 3D map built with Cesium. Before gITF and WebGL, Mr. Claus’s delivery route was much harder to trace.

Previous blog posts have stressed that the deployment process of neural networks to inference engines is becoming fragmented. An accepted standard can facilitate the industrial use of artificial intelligence by creating mutual compatibility between deep-learning frameworks and inference engines. The Neural Network Exchange Format (NNEF) is the Khronos Group’s solution to this problem.

There is a wide range of open-source deep learning training networks available today offering researchers and designers plenty of choice when they are setting up their project. Caffe, Tensorflow, Chainer, Theano, Caffe2, the list goes on and is getting longer all the time. This diversity is great for encouraging innovation, as the different approaches taken by the various frameworks make it possible to access a very wide range of capabilities, and, of course, to add functionality that’s then given back to the community. This helps to drive the virtuous cycle of innovation.

As part of the ongoing work to ensure glTF meets the needs of the developer community the Khronos™ 3D Formats working group is working on a new glTF compression extension to greatly improve transmission efficiency of texture assets while providing efficient, cross-platform transcoding into a wide range of GPU hardware-accelerated texture formats.

The Khronos™ Group is about to release a new standard method of moving trained neural networks among frameworks, and between frameworks and inference engines. The new standard is the Neural Network Exchange Format (NNEF™); it has been in design for over a year and will be available to the public by the end of 2017.