Random Access Data Compression Using Metadata Surfaces
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Solution Overview
Problem
Conventional data compression schemes for graphics processing units (GPUs) incur granularity loss and significant power dissipation due to unpredictable access patterns in real-time graphics processing, requiring efficient lossless compression and decompression of randomly accessed data to reduce memory bandwidth and power consumption.
Innovation Solution
A method and apparatus for lossless, real-time compression and decompression of randomly accessed data, using metadata surfaces with compression keys to dynamically compress and decompress data in a streaming manner, allowing independent compression of data blocks and random access for efficient memory usage and power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression schemes compress entire image surfaces at once, then compression ratio is improved, but random access capability deteriorates and power dissipation increases
Solution Approach 1:
The patent divides the image surface into multiple independently compressed blocks or tiles. Each block can be compressed and decompressed independently, enabling random access to specific regions without processing the entire image. This segmentation maintains high compression ratios for each block while allowing selective access based on GPU needs.
Solution Approach 2:
The patent implements dynamic compression where the compression and decompression process adapts to random access patterns. Metadata is generated to track compressed block locations and sizes, allowing the system to dynamically select and decompress only the necessary blocks based on real-time access requirements, rather than processing static entire-image structures.
2Reliability
If conventional data compression schemes read and decompress entire image surfaces, then complete image fidelity is maintained, but memory bandwidth consumption increases
Solution Approach 1:
The patent extracts and processes only the specific blocks or tiles that are currently needed by the GPU, rather than reading and decompressing the entire image surface. This selective extraction reduces memory bandwidth consumption significantly while maintaining full fidelity for the accessed regions through lossless compression.
Solution Approach 2:
The patent applies partial action by performing compression and decompression only on the necessary portions of the image surface at any given time. Instead of processing the complete image, the system processes just enough data to satisfy current GPU rendering needs, reducing overall memory bandwidth usage while maintaining complete fidelity for the partial data that is processed.
3Reliability
If lossless compression is applied to entire image surfaces, then image fidelity is preserved, but processing time and power consumption increase
Solution Approach 1:
The patent segments the image surface into independent blocks that can be compressed and decompressed separately. This allows the system to preserve full image fidelity through lossless compression of each block while reducing processing time by only compressing and decompressing the specific blocks that are currently needed, rather than processing the entire image surface.
4Stability of the object's composition
If conventional compression schemes process entire image surfaces, then compression consistency is maintained, but adaptability to random access patterns deteriorates
Solution Approach 1:
The patent segments the image into standardized blocks that maintain consistent compression formatting within each block, while the collection of blocks as a whole adapts to random access patterns. Each block maintains compression consistency through uniform encoding, while the metadata structure allows the system to adaptively select and process specific blocks based on varying access patterns.
Solution Approach 2:
The patent implements a dynamic system where the compression structure remains consistent at the block level, but the overall image surface can adapt to random access patterns through selective block processing. The metadata enables dynamic identification and retrieval of specific compressed blocks, allowing the system to maintain compression consistency while adapting to varying access requirements in real-time graphics processing.
Data Source
AI summary
A method and apparatus for real time compressing randomly accessed data includes extracting a block of randomly accessed data from a memory hierarchy. One or more individual portions of the randomly accessed data are independently compressed in real time to create a lossless compressed image surface. The compressed image surface includes data of independently compressed image blocks for reading and decompressing in a random order. The method further includes storing structured information relating to the dynamically compressed randomly accessed data.