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neural.slang: A Standard Module for Inline Neural Networks in Shaders

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Kai Zhang

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Description

neural.slang: A Standard Module for Inline Neural Networks in Shaders is a Hot3D conference talk by Kai Zhang introducing neural.slang, an experimental Slang standard library for evaluating and training compact neural networks directly inside shader code paths. Zhang positions the module as infrastructure for neural materials and other inline learned effects.

The talk explains encoder, layer, storage, and vector abstractions, portable fallback paths versus WaveTangled MMA-accelerated routes on CUDA, Vulkan, and Metal, and a path-traced neural material example that exercises training and inference in one framework. Shader authors see how Slang's type system keeps network definitions readable across APIs.

Advanced shader programmers comfortable with Slang or HLSL-like languages and basic autodiff concepts will follow best. Knowledge of tensor cores or subgroup matrix operations is useful but not mandatory for the overview sections.

Video of the Hot3D session is on YouTube; Slang and neural.slang sources ship via the Slang project repositories with documentation evolving post-talk.

Slang and NVIDIA graphics research talks on inline neural networks draw strong interest at SIGGRAPH-affiliated venues; practitioners experimenting with neural materials often reference Zhang's Hot3D introduction when adopting neural.slang.