GPU Tile Rendering Complexity Control via Cost Indications
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Solution Overview
Problem
Existing graphics processing systems face challenges in reducing latency during real-time rendering and display, particularly in virtual reality applications, due to the need to render tiles in a raster scan order ahead of the display.
Innovation Solution
A graphics processing unit (GPU) is configured to process graphics data by subdividing the rendering space into tiles and using cost indication logic to control the rendering complexity of each tile based on its processing cost, allowing for efficient rendering that meets timing constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If tiles are rendered in raster scan order ahead of display to reduce latency, then display latency is reduced, but rendering time constraints are difficult to meet when tiles have varying complexity
Solution Approach 1:
The patent applies local quality by differentiating rendering complexity across different tiles based on their content characteristics. Tiles containing complex graphics data (such as transparent objects, complex geometries, or detailed textures) are rendered with higher complexity, while simpler tiles are rendered with lower complexity. This allows the system to maintain high rendering throughput for simple regions while ensuring quality where needed, thereby meeting timing constraints for real-time display.
Solution Approach 2:
The patent implements preliminary action by determining the complexity of each tile before the rendering process begins. The system analyzes graphics data for each tile to classify it as simple or complex, and this classification is used to control the rendering complexity in advance. This preliminary classification allows the rendering system to optimize resource allocation and timing for each tile, ensuring that latency constraints are met while maintaining appropriate image quality.
2Manufacturing precision
If rendering complexity is increased to maintain image quality, then image quality is improved, but rendering time increases causing latency
Solution Approach 1:
The system applies different rendering quality levels to different regions of the image based on their complexity. High-quality rendering with multiple samples per pixel is applied only to tiles containing complex graphics data, while lower-quality rendering with fewer samples is applied to simpler tiles. This localized quality adjustment maintains image quality where it matters most while significantly reducing overall rendering time.
Solution Approach 2:
The patent changes the rendering parameter (number of samples per pixel) dynamically based on tile complexity. For simple tiles, the system uses a lower number of samples, while for complex tiles, it increases the number of samples. This parameter adjustment allows the system to optimize between image quality and rendering time for each individual tile, preventing unnecessary computation in simple regions while ensuring quality in complex regions.
Data Source
AI summary
A graphics processing unit (GPU) processes graphics data using a rendering space which is sub-divided into a plurality of tiles. The GPU comprises cost indication logic configured to obtain a cost indication for each of a plurality of sets of one or more tiles of the rendering space. The cost indication for a set of tile(s) is suggestive of a cost of processing the set of one or more tiles. The GPU controls a rendering complexity with which primitives are rendered in tiles based on the cost indication for those tiles. This allows tiles to be rendered in a manner that is suitable based on the complexity of the graphics data within the tiles. In turn, this allows the rendering to satisfy constraints such as timing constraints even when the complexity of different tiles may vary significantly within an image.