The OpenCL Working Group has published a draft extension (cl_khr_cooperative_matrix) that brings cooperative matrix operations—a key technology for accelerating ML inference—to OpenCL, developed in collaboration with Arm, Intel, and Qualcomm. A companion extension to expose these capabilities directly in the OpenCL C language is also in progress, and the community is invited to review both drafts and provide feedback before they are finalized.

SIGGRAPH ASIA is a pivotal gathering place for the global graphics community. This is where the industry pushes the boundaries of the state of the art and sets the direction for collaborative efforts. The Khronos® Group is presenting a future-focused slate of BOF presentations and activities at SIGGRAPH Asia 2025, taking place in Hong Kong, December 15 -18.

To further its goal of passing trained frameworks to embedded inference engines, the Khronos Group adds to its existing converters with two new bidirectional converters. Now available on the NNEF GitHub, these new tools enable easy conversion of trained models, including quantized models, between TensorFlow or Caffe2 formats and NNEF format.

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.

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.

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.