Train-Linking Numeric Compression Without Frequency-Table Overhead
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Solution Overview
Problem
Existing lossless data compression methods, particularly those using run-length and entropy coding, face challenges such as high latency, overhead from symbol frequency tables, and inefficiency in compressing data blocks with small variations or all-zero blocks, which limits their effectiveness for neural network weight and feature map compression.
Innovation Solution
The train-linking data compression system employs a multi-method approach that selects between Gaussian, Laplace, delta, same, and copy coding based on data block characteristics, eliminating the need for a symbol frequency table and enabling parallel implementation, thereby reducing latency and improving compression efficiency for various data distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If run-length coding and entropy coding are used, then lossless compression is achieved, but latency increases and parallel implementation becomes difficult
Solution Approach 1:
The patent divides the compression process into independent parallel blocks, where each block can be compressed simultaneously using multiple coding methods (run-length, entropy, copy, delta). This segmentation enables parallel implementation while maintaining lossless compression, resolving the contradiction between reliability and latency.
Solution Approach 2:
The system dynamically selects the most appropriate coding method for each data block based on its characteristics. By adapting the compression approach to match the data pattern (using run-length for repetitive data, entropy for random data, copy for identical blocks, delta for small variations), the system achieves both lossless compression and reduced latency through optimized processing.
2Reliability
If symbol frequency table is used for entropy coding, then compression is achieved, but overhead increases consuming bandwidth
Solution Approach 1:
The patent extracts and eliminates the symbol frequency table overhead by replacing entropy coding with alternative methods (copy coding, delta coding, run-length coding) that do not require transmitting frequency tables. This removes the harmful overhead while preserving compression efficiency for appropriate data types.
Solution Approach 2:
Instead of using entropy coding with frequency tables, the system uses copy coding for identical data blocks, where a simple reference to a previous block replaces the need for complex entropy encoding and frequency table transmission, significantly reducing overhead.
3Reliability
If run-length coding is used, then compression is achieved, but search window size increases creating a bottleneck
Solution Approach 1:
The patent segments the data processing into fixed-size blocks that are processed independently. This limits the search window to within each block boundaries, reducing the complexity bottleneck while maintaining run-length compression capability within those segmented units.
Solution Approach 2:
The system applies different compression methods to different local blocks based on their characteristics. Run-length coding is applied locally to blocks that benefit from it, rather than requiring a global search window across the entire data set, thereby reducing complexity while preserving compression effectiveness.
4Reliability
If classical two-stage encoding system is used, then compression is achieved, but parallel implementation becomes difficult
Solution Approach 1:
The patent divides the data into independent blocks that can be processed in parallel, with each block undergoing multiple coding attempts simultaneously. This segmentation transforms the sequential two-stage process into a parallel architecture where multiple coding methods compete for each block independently, dramatically improving productivity.
Solution Approach 2:
The system implements a universal compression framework that applies multiple coding methods (run-length, entropy, copy, delta) to each data block, selecting the best performer. This multi-functional approach allows parallel evaluation of different coding strategies, enhancing both compression effectiveness and parallel implementation capability.
Data Source
AI summary
A train-linking lossless data compressor examines a block of data and uses a same coder to generate a same code when all data values in the input block are identical. When the input data is not all the same value, then a Gaussian coder, a Laplace coder, and a delta coder are activated in parallel. The three compressed code lengths are compared and the smallest code length is output as the compressed code when it is smaller than a copy code length. The copy code is a tag followed by copying all the data in the input block. When the smallest of the three compressed code lengths is larger than the copy code length, the file is not compressible, and the copy code is output. No frequency table is required so latency is low. The delta coder subtracts data values from an average value of the last data block.