Intermediate Compressed Data Mapping for Random-Access GPU Compression
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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, especially 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 space are reduced, but compression ratio varies and random access becomes inefficient
Solution Approach 1:
The patent segments data into fixed-size blocks that are independently compressed and stored. Each block is processed separately through compression circuits, allowing consistent compression ratios while enabling random access to individual blocks without decompressing entire data sets. This segmentation resolves the contradiction by making compression predictable and access efficient.
Solution Approach 2:
The patent performs preliminary compression of data blocks before storage, organizing them into a structured format with headers indicating block sizes and offsets. This preliminary organization enables both consistent compression ratios and efficient random access, as the compressed structure is prepared in advance for predictable performance characteristics.
2Measurement precision
If higher quality rendering algorithms are used on faster GPUs, then rendering quality improves, but memory bandwidth consumption increases
Solution Approach 1:
The patent changes the parameter of data representation by compressing rendering data (pixel data, depth data, texture data) into smaller formats. This parameter change reduces memory bandwidth consumption while maintaining rendering quality, as the compressed data can be decompressed on-demand during rendering operations, allowing high-quality algorithms to run with lower memory bandwidth requirements.
3Productivity
If memory bandwidth is increased to support higher quality rendering, then rendering performance improves, but power consumption increases
Solution Approach 1:
The patent changes the data format parameter by implementing compression that reduces the volume of data transferred across the memory bus. This parameter change maintains rendering performance by ensuring data is available when needed, while reducing power consumption by minimizing memory bandwidth usage, which is a major power consumer in GPU systems.
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.