Matrix Data Representation Format Selection for Moderate Sparsity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for representing matrix data are ineffective for data with moderate sparsity, as they only distinguish between high and low sparsity to choose between dense and sparse formats, failing to select the optimal representation format for matrix data with moderate sparsity.
Innovation Solution
An information processing apparatus and method that acquires matrix data, calculates its sparsity, and selects from multiple representation formats, including dense and sparse formats, to determine the most suitable format for the data, enabling effective representation of matrix data with moderate sparsity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only two representation formats (dense and sparse) are considered with a single threshold, then the selection process is simple, but matrix data with moderate sparsity cannot be effectively represented
Solution Approach 1:
The patent segments the representation format selection into multiple tiers by introducing intermediate sparse representation formats between dense and traditional sparse formats. This creates a graduated scale of representation options that can better match matrix data with varying degrees of sparsity, particularly addressing the gap for moderate sparsity cases.
Solution Approach 2:
The patent changes the parameter of representation granularity by introducing multiple sparse representation formats with different levels of compression and storage efficiency. This allows the system to adapt the representation parameters to match the actual sparsity characteristics of the matrix data, improving representation accuracy for moderate sparsity cases.
2Manufacturing precision
If multiple sparse representation formats are introduced, then moderate sparsity can be handled, but the complexity of format selection increases
Solution Approach 1:
The patent implements dynamic threshold adjustment based on the actual sparsity characteristics of the input matrix data. The system calculates the sparsity of the data and dynamically selects appropriate thresholds from multiple levels, allowing the format selection process to adapt to different data characteristics without requiring complex manual configuration.
Solution Approach 2:
The system incorporates feedback mechanisms by calculating the sparsity of matrix data and using this information to guide the selection of appropriate representation formats. The sparsity calculation provides feedback about the data characteristics, which then informs the threshold selection and format determination process.
Data Source
AI summary
The information processing apparatus includes an acquisition unit, a sparsity calculation unit, a selection unit, and an output unit. The acquisition unit acquires the input matrix data information. The sparsity calculation unit calculates the sparsity of the target matrix data represented by the input matrix data information. The selection unit selects a representation format to be applied to the output matrix data information from a plurality of representation formats, based on the sparsity calculated by the sparsity calculation unit. The plurality of representation formats include the dense representation format and at least two sparse representation formats. The output unit outputs the output matrix data information that represents the target matrix data in the representation format selected by the selection unit.