Qualcomm releases ANF SDK, bringing AI super resolution and frame generation to Vulkan

Qualcomm recently released the Adreno Neural Fusion (ANF) SDK, bringing AI super resolution and frame generation into Vulkan rendering workflows for mobile games. According to Qualcomm, Adreno GPUs with matrix cores can read existing image buffers directly and keep intermediate tensors in on-chip memory, reducing transfers between processors and external memory accesses. [Technical announcement](https://www.qualcomm.com/developer/blog/2026/09/introducing-adreno-neural-fusion-sdk-for-snapdragon-mobile-platforms)

Super resolution requires color and depth rendered with projection jitter, along with motion vectors that exclude the jitter offset, to reconstruct detail using information from successive frames. In the performance mode listed in the integration documentation, the output width and height are each twice those of the input. Motion vectors must also capture the apparent movement of static objects caused by camera motion; otherwise, previous frames may be reprojected to incorrect positions. [Integration guide](https://github.com/SnapdragonGameStudios/adreno-neural-fusion/blob/main/INTEGRATION-GUIDE.md)

Integrating frame generation also introduces resource dimension constraints: its inputs and outputs must match the dimensions specified at creation, so lower-resolution depth and motion vectors with different dimensions cannot be passed directly to it. Elements such as UI text and crosshairs should be composited separately to keep them out of temporal accumulation or interpolation. These requirements affect the arrangement of rendering stages and buffer allocation. [Resource specifications](https://github.com/SnapdragonGameStudios/adreno-neural-fusion/blob/main/INTEGRATION-GUIDE.md)

The application is also responsible for synchronization. After a call in recording mode, developers must treat the command buffer’s binding state as undefined and rebind the pipelines and resources needed for subsequent operations. Immediate mode coordinates execution through semaphores, but image memory dependencies still need to be handled. Integration therefore involves the entire rendering workflow. [Synchronization requirements](https://github.com/SnapdragonGameStudios/adreno-neural-fusion/blob/main/INTEGRATION-GUIDE.md)

The publicly available package includes Android ARM64 runtime libraries, headers, and documentation. The README lists Snapdragon 8 Elite Gen 6 and later platforms, while also requiring runtime queries for feature support and a fallback path. The headers use the BSD 3-Clause License, while the precompiled runtime libraries use a separate Qualcomm binary license, so the package cannot be considered a fully open-source implementation. [Package contents](https://github.com/SnapdragonGameStudios/adreno-neural-fusion)

The SDK also includes a debug overlay for inspecting depth, motion vectors, and reprojection errors, though it currently supports only super resolution. Enabling it also changes the usage flags required for image resources, which must be accounted for before those resources are created. [Debug documentation](https://github.com/SnapdragonGameStudios/adreno-neural-fusion/blob/main/DEBUG-OVERLAY.md)

Qualcomm states that the initial version inserts one generated frame for every rendered frame and adds one rendered frame’s worth of input latency. Engineering evaluations should therefore measure control latency, sustained power consumption, and visual artifacts during fast motion. A higher display refresh rate does not directly indicate how much responsiveness or battery life will improve. [Frame generation limitations](https://www.qualcomm.com/developer/blog/2026/09/introducing-adreno-neural-fusion-sdk-for-snapdragon-mobile-platforms)