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.