AI Model Decompression Decoder for Parallel Sparse Weight Recovery
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Solution Overview
Problem
Existing methods for compressing deep learning models, such as pruning and quantization, face challenges in achieving a higher compression ratio while maintaining performance, particularly in parallel computing and decompression speed.
Innovation Solution
An electronic apparatus is designed with a memory to store compressed data, a decoder featuring a shift register and logic circuits for decompressing data using XOR gates, and a processor to obtain decompressed data suitable for neural network calculations. This apparatus includes mechanisms for updating decompressed data based on patch information and using representative value matrices to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If pruning and quantization are used to compress deep learning models, then the data size is reduced and hardware requirements are lowered, but the compression ratio is limited and index size becomes excessively large
Solution Approach 1:
The patent segments the weight matrix into multiple blocks and applies different compression techniques to each block. Specifically, it divides the weight matrix into sub-matrices, applies pruning and quantization to each block separately, and uses block-sparse formats to reduce the overall index size while maintaining compression efficiency.
Solution Approach 2:
The patent transforms the traditional CSR (Compressed Sparse Row) format into a block-based sparse format by adding a dimensional grouping structure. This block-dimension organization allows the system to manage non-zero elements in grouped units, reducing the overhead of individual element indexing while preserving the sparse structure benefits.
2Quantity of substance
If CSR format is used to store sparse matrices, then non-zero values are efficiently stored, but the format is not suitable for parallel computing and decompression speed is slow
Solution Approach 1:
The patent divides the sparse matrix into multiple independent blocks that can be processed in parallel. Each block maintains its own compressed structure, allowing simultaneous decompression of different blocks using multiple processing units, thereby significantly improving decompression throughput while maintaining storage efficiency.
Solution Approach 2:
The patent introduces a dynamic block processing mechanism where the system can adaptively select which blocks to process based on computational requirements. The block-based structure enables flexible parallelization strategies, allowing the decompression process to dynamically utilize available computational resources for optimized performance.
3Quantity of substance
If higher compression ratios are achieved through aggressive pruning, then model size is reduced, but prediction accuracy degrades
Solution Approach 1:
The patent applies different pruning thresholds and compression strategies to different blocks of the weight matrix based on their importance and sparsity characteristics. Critical blocks maintain higher precision with less aggressive pruning, while less critical blocks undergo more aggressive compression, thereby maintaining overall model accuracy while achieving higher compression ratios.
Solution Approach 2:
The patent dynamically adjusts pruning thresholds and quantization precision parameters based on the specific characteristics of each weight block. By adapting these parameters locally rather than applying uniform compression across the entire model, the system achieves better accuracy-compression trade-offs compared to global compression approaches.
Data Source
AI summary
An electronic apparatus may include a memory configured to store compressed data that is to be decompressed for a neural network calculation of an artificial intelligence model; a decoder including a shift register configured to sequentially receive the compressed data in group units and output at least two groups of the compressed data, and a plurality of logic circuits configured to decompress the at least two groups of the compressed data to obtain decompressed data; and a processor configured to obtain the decompressed data in a form capable of being calculated by a neural network.