Mixed-Compressibility Data Compression for Floating-Point Models
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Solution Overview
Problem
Data compression techniques struggle with mixed compressibility, where some data is highly compressible while others are not, leading to inefficient memory usage and potential information loss, especially in floating-point data representing machine-learned model parameters.
Innovation Solution
A method that separates floating-point data into compressible and uncompressible portions, using predictive techniques for the former and Kashin Decomposition combined with quantization for the latter, to effectively reduce data size while minimizing information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a single compression technique is applied to all data, then the compressible portion can be reduced in size, but the uncompressible portion suffers information loss
Solution Approach 1:
The patent divides the data into two distinct portions: a compressible portion and an uncompressible portion. This segmentation allows different compression techniques to be applied to each portion appropriately, preventing information loss in the uncompressible portion while achieving compression in the compressible portion.
Solution Approach 2:
The patent applies different compression strategies to different parts of the data based on their local characteristics. The compressible portion receives predictive compression techniques, while the uncompressible portion is handled with techniques that preserve information integrity, such as Kashin decomposition combined with quantization.
2Productivity
If predictive techniques are used on compressible data, then compression efficiency improves, but the technique fails on uncompressible data
Solution Approach 1:
The patent segments data into compressible and uncompressible portions, allowing predictive techniques to be applied only where they are effective (compressible portion), while alternative techniques are used for uncompressible data, ensuring overall reliability.
Solution Approach 2:
The patent changes the approach based on data characteristics by using Kashin decomposition and quantization for uncompressible portions, transforming the data representation to achieve reliable compression without relying on predictive techniques that would fail.
3Loss of information
If Kashin Decomposition with quantization is applied to uncompressible data, then information loss is minimized, but the process is computationally complex
Solution Approach 1:
The patent isolates the computationally complex Kashin decomposition and quantization process to only the uncompressible portion of the data, rather than applying it to all data. This reduces overall processing complexity while maintaining information integrity where needed.
Solution Approach 2:
The patent applies the complex Kashin decomposition technique partially, only to the extent necessary for the uncompressible portion, rather than excessively applying it to all data. This balances information preservation with computational efficiency.
Data Source
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AI summary
Systems and methods for compression of data that exhibits mixed compressibility, such as floating-point data, are provided. As one example, aspects of the present disclosure can be used to compress floating-point data that represents the values of parameters of a machine-learned model. Therefore, aspects of the present disclosure can be used to compress machine-learned models (e.g., for reducing storage requirements associated with the model, reducing the bandwidth expended to transmit the model, etc.).