UV Space Rendering for Neural Scaling
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Solution Overview
Problem
Current graphics processing technologies face challenges in efficiently processing UV space data for machine learning models, as they are trained primarily on screen space data, which is more difficult for these models to comprehend.
Innovation Solution
The implementation of UV space rendering techniques that decouple rasterization, allowing for the processing of frame data in UV space, enabling neural scaling and efficient transmission of visual data across networks, particularly in cloud gaming systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If screen space data is used for machine learning model training, then the data is easily comprehended by humans, but it becomes more difficult for machine learning models to comprehend
Solution Approach 1:
The patent introduces UV space as an intermediary representation that bridges human-comprehensible screen space and machine-learning-friendly feature space. The UV coordinates serve as a mediator that preserves geometric relationships while being more suitable for neural network processing, allowing both human visualization and machine learning comprehension
2Manufacturing precision
If traditional screen space rendering is used, then rendering quality is maintained, but computational overhead increases and neural scaling efficiency decreases
Solution Approach 1:
The patent transitions from 2D screen space coordinates to 3D UV space coordinates, adding a dimensional perspective that enables more efficient neural network processing. This dimensional change allows the rendering pipeline to maintain quality while improving computational efficiency for AI upscaling operations
Solution Approach 2:
The patent decouples the rendering pipeline into separate stages: traditional rasterization for geometric accuracy, followed by UV space processing for neural network input. This segmentation allows each component to optimize for its specific function, maintaining rendering quality while improving overall computational efficiency
Data Source
AI summary
Described herein are techniques to render frame data in UV space and process the UV space data via a machine learning model. One embodiment provides an apparatus including a parallel processor having first circuitry configured to execute operations associated with a three-dimensional (3D) application programming interface (API) to render scene data for a frame in a UV coordinate space, second circuitry configured to execute instructions to perform a matrix multiply accumulate operation associated with a machine learning model that is trained to process the scene data in the UV coordinate space to generate processed scene data in the UV coordinate space, and third circuitry to rasterize the processed scene data in the UV coordinate space into a screen space representation of the scene data.