Intermediate Data Compression for Guaranteed GPU Memory Reduction
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Solution Overview
Problem
Current data compression methods in graphics processing units (GPUs) face challenges in reducing memory bandwidth and storage space efficiently, particularly in mobile devices, due to varying compression ratios and the need for random access to compressed data, which can lead to inefficiencies in memory allocation and power consumption.
Innovation Solution
The implementation of a data compression and decompression unit that uses a combination of lossless and lossy compression techniques, including truncation, bit replication, and adjustment values, to ensure a guaranteed compression threshold is met, allowing for efficient mapping of n-bit numbers to m-bit numbers, thereby reducing memory bandwidth and storage requirements while maintaining random access capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to reduce memory bandwidth and storage space, then memory bandwidth and storage requirements are reduced, but compression ratio variability and random access inefficiencies occur
Solution Approach 1:
The patent segments the compression process into multiple independent stages: a lossy compression stage that provides guaranteed compression ratio, and a lossless compression stage that achieves additional compression. This segmentation allows each stage to have deterministic behavior, ensuring consistent overall compression ratio while maintaining the ability to meet memory bandwidth requirements.
Solution Approach 2:
The lossy compression stage is applied first as a preliminary action to reduce data size with a guaranteed compression ratio threshold. This preliminary compression creates a smaller intermediate dataset that then undergoes lossless compression, ensuring that the final compression ratio meets the required threshold while maximizing overall compression efficiency.
2Quantity of substance
If data compression is applied to reduce storage space, then storage requirements are reduced, but random access capabilities deteriorate
Solution Approach 1:
The patent segments compressed data into multiple blocks with headers that contain metadata about the compressed content. This segmentation enables random access to specific blocks without decompressing the entire dataset, maintaining random access capability while achieving significant storage space reduction through the combined lossy and lossless compression approach.
3Measurement 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 patent changes the precision parameters of stored data by applying lossy compression that reduces bit depth while maintaining visual quality within acceptable thresholds. This parameter change allows high-quality rendering algorithms to operate with reduced memory bandwidth requirements, as the compressed data occupies less memory space and requires fewer memory transfers.
Data Source
AI summary
Methods for converting an n-bit number into an m-bit number for situations where n>m and also for situations where n<m, where n and m are integers. The methods use truncation or bit replication followed by the calculation of an adjustment value which is applied to the replicated number.