Mixed-Compressibility Data Compression for ML Model Parameters
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Solution Overview
Problem
Existing data compression techniques are ineffective for mixed compressibility data, particularly floating-point data, as they either result in significant information loss or limited memory reduction, especially when dealing with machine-learned model parameters, where a significant portion of the data exhibits low compressibility.
Innovation Solution
A method that separates data into compressible and uncompressible portions, using predictive techniques for the compressible part and Kashin Decomposition combined with quantization for the uncompressible part, to minimize error distribution and achieve efficient compression.
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 from significant information loss or limited memory reduction
Solution Approach 1:
The patent divides the data into two distinct portions: a compressible portion and an uncompressible portion. Different compression techniques are applied to each portion according to its characteristics. The compressible portion uses predictive compression techniques, while the uncompressible portion uses Kashin Decomposition combined with quantization. This segmentation allows each portion to be compressed optimally without suffering from information loss or limited memory reduction.
2Loss of information
If lossless compression is applied to uncompressible data, then information loss is minimized, but memory reduction is limited
Solution Approach 1:
The patent changes the approach by applying Kashin Decomposition to transform the uncompressible data into a different representation, followed by quantization that reduces precision in a controlled manner. This parameter change allows achieving both memory reduction and acceptable information preservation by transforming the data into a form that is more amenable to compression while maintaining essential characteristics.
3Quantity of substance
If aggressive compression is applied to reduce memory usage, then memory reduction is achieved, but error distribution increases and model accuracy deteriorates
Solution Approach 1:
The patent applies different compression strategies to different portions of the data based on their local characteristics. The compressible portion undergoes predictive compression that preserves accuracy, while the uncompressible portion uses Kashin Decomposition with quantization that controls error distribution. This local quality approach ensures that each portion is compressed in a way that maintains overall model accuracy while achieving memory reduction.
Data Source
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.).