Sparse Matrix Format Conversion for Efficient Vector Multiplication
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Solution Overview
Problem
Existing technologies face inefficiencies in processing sparse matrices due to suboptimal storage formats, leading to increased computational operations and reduced memory bandwidth.
Innovation Solution
A host processor converts sparse matrices from a first compressed format to a second optimized format using machine learning to determine the most efficient storage format based on matrix features, and preprocesses vectors for efficient multiplication by a computing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed compressed storage format is used for sparse matrices, then the storage structure is simple, but the computational efficiency is reduced due to suboptimal format for different matrix characteristics
Solution Approach 1:
The patent implements dynamic selection of compressed storage formats based on sparse matrix characteristics. The system evaluates sparsity patterns and dynamically chooses the most appropriate format (CSR, CSC, COO, etc.) rather than using a fixed format, thereby optimizing computational efficiency for different matrix types while managing conversion complexity through automated selection algorithms
Solution Approach 2:
The patent changes the storage format parameters of sparse matrices based on their specific characteristics. By analyzing matrix properties such as sparsity distribution and non-zero element patterns, the system transforms the storage format parameters to match the optimal format for each matrix, improving computational performance without manual intervention
2Productivity
If data is preprocessed and converted to optimal format, then computational operations are minimized, but additional processing time and memory bandwidth are required for conversion
Solution Approach 1:
The patent performs preliminary conversion of sparse matrices to their optimal compressed storage format before computational operations. By analyzing matrix characteristics and converting to the most suitable format in advance, the system minimizes the number of operations required during actual computation, achieving net time savings despite the initial conversion overhead
Solution Approach 2:
The system automatically analyzes sparse matrix characteristics and selects the optimal storage format without external intervention. The conversion process is self-managed through automated algorithms that evaluate matrix properties and perform format transformation, reducing the need for manual optimization and enabling scalable deployment
Data Source
AI summary
An electronic device includes a host processor configured to: convert a sparse matrix compressed and expressed in a first compressed format into a second compressed storage format, based on a feature of the sparse matrix; preprocess a vector based on the second compressed storage format; and transmit the sparse matrix converted into the second compressed storage format and the preprocessed vector to a computing device; and the computing device configured to multiply the sparse matrix converted into the second compressed storage format by the preprocessed vector.