Khronos royalty-free open standards for 3D graphics, Virtual and Augmented Reality, Parallel Computing, Machine Learning, and Vision Processing

AMD has released FSR SDK 2.4, extending its ML-based FSR Upscaling 4.1.2 to Radeon RX 6000 Series GPUs and select Ryzen processors with integrated graphics. The update also brings FSR upscaling and frame generation on Vulkan to feature parity with DirectX 12, with the exception of Intel PresentMon reporting. Vulkan developers integrate the SDK directly, and its samples target Vulkan 1.2. Binaries and documentation are available now on GitHub and GPUOpen.

SGDL Innovation SA is a Swiss-Canadian deeptech company whose technology describes 3D shapes precisely as compact mathematical forms rather than triangle meshes. Each shape is stored only once and can be rendered at any level of detail, from embedded devices to the cloud, including on low-power processors. Used since 2003 in the ISS robotic arm simulator, the technology has contributed to standards since MPEG-4 (AFX). SGDL is also a member of AOUSD and OGC. At Khronos, SGDL aims to bring this technology to ANARI and glTF.

In this Qualcomm Developer Blog tutorial, Zimu Guan shows how Android apps can share GPU memory and coordinate work directly between Vulkan and OpenCL on Adreno GPUs, avoiding CPU copies when a Vulkan renderer hands data to OpenCL kernels for compute, ML, denoising or super-resolution. The post walks through a full Vulkan to OpenCL to Vulkan workflow using Khronos external memory and external semaphore extensions. Vulkan exports allocations as opaque fds that OpenCL imports, with Qualcomm’s cl_qcom_external_memory_vulkan_opaque_fd extension handling optimal-tiling images. Synchronization uses one-shot sync-fd semaphores, with a fresh export and import at each handoff. It includes code for each step and points to the opencl_interop sample in the Adreno GPU Vulkan Code Sample Framework. Support is device- and driver-dependent, so apps should rely on runtime capability queries, and a follow-up on Android Hardware Buffer interop is planned.

Apex Compute is building high-performance, low-power AI chips for edge devices and developing GPU architecture. Their AI accelerator is designed to seamlessly run modern transformer models directly on devices like robots, drones, smart cameras, wearables, and autonomous vehicles. Apex Compute's GPU architecture is being developed with the goal of Vulkan conformance and already runs graphics workloads at the FPGA prototyping stage. They are creating more efficient alternatives to traditional computing architectures, with a focus on low latency, strong real-world performance, practical deployment, and architectural efficiency.

GRENETA is a South Korean deep-tech company developing 3D spatial data infrastructure for Physical AI, robotics, and industrial simulation. Their technology transforms real-world environments into high-fidelity, lightweight 3D assets and scalable spatial datasets, bridging the gap between physical-world capture and AI-driven simulation. At the core of GRENETA's technology is their proprietary GRENETA Optimizer, which addresses one of the major bottlenecks in large-scale 3D applications: excessive data size, transmission costs, and GPU memory consumption. In selected tests, their technology has achieved up to 99.6% data size reduction while preserving high visual fidelity.

Beyond compression, GRENETA is developing an integrated workflow encompassing 3D reconstruction, optimization, format conversion, and spatial data structuring. Their long-term objective is to enable Simulation-Ready and Robot-Ready 3D datasets, incorporating object-level semantics, spatial relationships, and physics-relevant attributes for Physical AI applications. GRENETA's vision is to make real-world 3D environments computationally efficient, accessible, and usable as foundational spatial data for the next generation of Physical AI.

The Khronos OpenXR Working Group has released a new set of extensions that establish an open, cross-platform API for spatial windowing systems. The extensions introduce "spatial containers" to OpenXR, which are system-managed spaces in which applications can display XR content, similar to how traditional applications draw 2D content within windows on a desktop. Spatial containers give developers a standardized way to create applications that can move between immersive experiences and multitasking with the real world or other apps across compatible XR platforms.