GPU Data Compression Switching for Guaranteed Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data compression methods in graphics processing units (GPUs) face challenges in balancing memory bandwidth and quality, particularly in mobile devices, where power consumption is a concern, and existing methods may not effectively guarantee a target compression threshold, leading to inefficiencies in memory space and bandwidth usage.
Innovation Solution
A method involving a primary and reserve compression unit, using lossless and lossy compression techniques respectively, to ensure a target compression threshold is met, with a test unit assessing the compressed data block and selecting the appropriate output to optimize memory usage and power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If lossless compression technique is used to maintain data quality, then data quality is improved, but compression ratio deteriorates (compression threshold not satisfied)
Solution Approach 1:
The system changes the compression parameter by switching between lossless and lossy compression techniques based on whether the compression threshold is satisfied. When lossless compression fails to meet the threshold, the system transitions to lossy compression with adjustable quality factors to achieve the required compression ratio while maintaining acceptable data quality
Solution Approach 2:
The compression system dynamically selects between lossless and lossy compression modes based on real-time assessment of compression threshold satisfaction. This dynamic switching allows the system to adapt to different data characteristics and threshold requirements, optimizing both data quality and compression ratio
2Manufacturing precision
If higher quality rendering algorithms are used on faster GPUs, then rendering quality is improved, but memory bandwidth consumption increases
Solution Approach 1:
The system changes the parameter of data representation by compressing image data before storage and transfer in memory. This parameter change reduces the quantity of data moving through the memory subsystem, thereby reducing memory bandwidth consumption while maintaining rendering quality through appropriate compression techniques
Solution Approach 2:
The compression operation is performed preliminarily before data is stored in or transferred from memory. This preliminary compression reduces the data volume that needs to be handled by the memory subsystem, thereby reducing memory bandwidth requirements before the data even enters the rendering pipeline
3Volume of stationary object
If data is compressed before storage in memory, then memory space usage is reduced, but compression processing time increases
Solution Approach 1:
The compression system is segmented into multiple independent compression units that can process different data blocks in parallel. This segmentation allows compression processing to be distributed across multiple processing elements, reducing the overall processing time while maintaining effective compression ratios for memory space optimization
Data Source
AI summary
A method of compressing data is described in which the compressed data is generated by either or both of a primary compression unit or a reserve compression unit in order that a target compression threshold is satisfied. If a compressed data block generated by the primary compression unit satisfies the compression threshold, that block is output. However, if the compressed data block generated by the primary compression unit is too large, such that the compression threshold is not satisfied, a compressed data block generated by the reserve compression unit using a lossy compression technique, is output.