Threshold-Based Bit-Depth Remapping for Guaranteed Compression
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Solution Overview
Problem
Current data compression methods for GPUs and other processing units face challenges in reducing memory bandwidth and storage space efficiently, particularly in achieving consistent compression ratios across varying data blocks, which affects memory access latency and power consumption.
Innovation Solution
A method of data compression that determines the bit depth of input data by comparing its total size to pre-defined thresholds, using a mapping parameter to reduce bit depth and encode it within the compressed data, ensuring a guaranteed compression threshold is met, thereby optimizing memory usage and power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data compression is applied to reduce memory bandwidth and storage space, then memory usage and power consumption are improved, but compression ratio consistency across varying data blocks deteriorates
Solution Approach 1:
The patent performs preliminary analysis of the data block to determine its characteristics (such as variance, gradient, or other statistical properties) before applying compression. Based on this preliminary assessment, the most appropriate compression algorithm is selected, ensuring consistent compression ratios across different data types while maintaining energy efficiency.
Solution Approach 2:
The patent implements a dynamic compression system that adapts the compression ratio and algorithm selection based on the actual content of each data block. By dynamically adjusting compression parameters according to data characteristics, the system achieves both energy savings and consistent compression performance across varying data blocks.
2Measurement 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 data representations to match the actual requirements of the rendering algorithms. By using variable precision arithmetic and data formats that adapt to the specific rendering quality level needed, the system maintains high rendering quality while significantly reducing memory bandwidth consumption compared to using fixed high-precision formats throughout.
Data Source
AI summary
A method of data compression in which the total size of the compressed data is determined and based on that determination, the bit depth of the input data may be reduced before the data is compressed. The bit depth that is used may be determined by comparing the calculated total size to one or more pre-defined threshold values to generate a mapping parameter. The mapping parameter is then input to a remapping element that is arranged to perform the conversion of the input data and then output the converted data to a data compression element. The value of the mapping parameter may be encoded into the compressed data so that it can be extracted and used when subsequently decompressing the data.