Intermediate Compressed Data Mapping for GPU Memory Bursts
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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 while maintaining efficient random access and compression ratios, especially when dealing with varying block sizes and fixed burst sizes, which can lead to inefficiencies in memory transfer and quality degradation.
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, and incorporating pre-processing steps to convert data formats like 10-bit to 8-bit formats for enhanced compression.
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 random access efficiency and compression ratio consistency may deteriorate
Solution Approach 1:
The patent segments compressed data into fixed-size blocks that align with memory burst transfer sizes. This segmentation allows the compression unit to process and compress data in standardized chunks, ensuring that compressed data can be efficiently transferred in full bursts while maintaining random access capability through block-level addressing. The segmented block structure resolves the contradiction by organizing compressed data in a way that is simultaneously optimized for both reduced bandwidth usage and efficient random access.
Solution Approach 2:
The patent performs preliminary compression of data blocks before storage, ensuring that the compressed data meets guaranteed compression thresholds. By pre-compressing data into fixed-size blocks with guaranteed compression ratios, the system eliminates the need for runtime compression decisions that would hinder random access efficiency. This preliminary action resolves the contradiction by preparing data in advance in a format that optimizes both bandwidth reduction and access efficiency.
2Quantity of substance
If data compression is applied to reduce storage space, then storage requirements are reduced, but quality degradation may occur
Solution Approach 1:
The patent employs dynamic compression techniques that adjust compression parameters based on data characteristics while maintaining guaranteed compression thresholds. The compression unit dynamically selects from multiple compression strategies (lossless, lossy with quality control, and hybrid approaches) to achieve the required compression ratio while preserving data quality within acceptable bounds. This dynamic adaptation resolves the contradiction by flexibly balancing compression ratio and quality based on the specific data being compressed.
Solution Approach 2:
The patent changes compression parameters such as precision levels, block sizes, and compression algorithms based on data type and required quality thresholds. For example, the system may use higher precision for critical data elements and lower precision for less critical elements, or adjust block sizes to optimize both compression ratio and quality. These parameter changes resolve the contradiction by allowing the system to achieve significant storage reduction while maintaining data quality through intelligent parameter selection.
3Productivity
If fixed burst sizes are used for memory transfer, then memory transfer efficiency is improved, but adaptability to varying block sizes deteriorates
Solution Approach 1:
The patent segments data into fixed-size blocks that match memory burst transfer sizes, creating a standardized interface between the compression unit and memory subsystem. This segmentation allows the system to always transfer data in full bursts, maximizing memory transfer efficiency regardless of the original data size. The fixed block segmentation resolves the contradiction by providing a consistent transfer unit that works efficiently with fixed burst sizes while still being able to handle varying data volumes through multiple blocks.
Solution Approach 2:
The patent introduces a block size dimension that separates the logical data size from the physical transfer size. By organizing data into fixed-size blocks for transfer purposes while maintaining the ability to handle variable-length data streams, the system adds a dimensional layer of abstraction. This allows fixed burst transfers to efficiently move data while the system remains adaptable to varying data requirements through the block structure, resolving the contradiction between transfer efficiency and adaptability.
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.