Tile-Based Graphics Processing Bin Stream Optimization
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Solution Overview
Problem
Graphics processing units in portable devices face challenges in achieving high graphics processing performance due to low processing capability and high power consumption, leading to inefficiencies in tile-based graphics rendering, particularly with increased resolution and tile numbers, which results in excessive bin stream generation and memory overheads.
Innovation Solution
A method is introduced that classifies tiles into scalable groups, generates a coverage pattern, and creates a bin stream using a run-length algorithm, allowing for efficient rendering by adjusting tile group sizes based on cache availability and resolution, thereby reducing the size of the bin stream and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the number of tiles is increased to support higher resolution rendering, then rendering quality is improved, but bin stream size and memory overhead increase
Solution Approach 1:
The image is divided into multiple tiles, and each tile is further organized into tile groups. This hierarchical segmentation allows the system to manage rendering at different levels of granularity, generating bin streams at the tile group level rather than individual tile level, thereby reducing overall bin stream size while maintaining rendering quality through fine-grained control where needed
Solution Approach 2:
The patent introduces a new organizational dimension by grouping tiles into tile groups with configurable sizes. This adds a intermediate level between individual tiles and the entire image, allowing bin streams to be generated and managed at the tile group level, which reduces the total number of bin stream entries compared to processing each tile individually
2Manufacturing precision
If the number of tiles is increased to support higher resolution rendering, then rendering quality is improved, but memory overhead increases
Solution Approach 1:
By segmenting the rendering workload into tile groups rather than individual tiles, the patent reduces the number of memory allocations and data structures needed. The bin stream is generated at the tile group level, which decreases the total memory overhead associated with storing and managing rendering state for each tile
Solution Approach 2:
Adjacent tiles with similar rendering characteristics are merged into the same tile group, allowing them to share common bin stream entries and rendering state. This merging reduces redundant memory usage while preserving the ability to render each tile with appropriate detail
3Manufacturing precision
If processing is performed at fine-grained tile level, then rendering precision is improved, but processing overhead increases
Solution Approach 1:
The patent implements dynamic tile group sizing where the granularity of tile groups can be adjusted based on rendering requirements and available resources. This allows the system to use coarser-grained tile groups when processing overhead is a concern, and finer-grained groups when rendering precision is prioritized, providing flexible adaptation to different operating conditions
Data Source
AI summary
A method and device for processing graphics data include generating primitives including objects in an image, classifying tiles dividing the image into scalable tile groups, generating a coverage pattern identifying primitives that are covered on tiles included in each scalable tile group, generating a bin stream including the coverage pattern corresponding to the each scalable tile groups, and performing rendering for each tile of the tiles using the bin stream.