Speech Recognition Model Quantization for Irregular Weight Blocks
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Solution Overview
Problem
The deployment of large-scale neural network models for speech recognition is hindered by high graphics memory usage and computational complexity, which existing quantization methods fail to address effectively due to the irregular distribution of parameters and poor quantization effects.
Innovation Solution
A method and apparatus that involves dividing the weight matrix of a speech recognition model into groups based on block importance, adjusting the order of these blocks, and quantizing them accordingly to minimize performance loss during the quantization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of parameters of the neural network model is increased to improve model capabilities, then the model performance is improved, but the graphics memory usage and computational complexity increase
Solution Approach 1:
The weight matrix is divided into multiple blocks, and each block is further divided into groups. This segmentation allows selective quantization of different blocks based on their importance to the model, enabling reduction of overall memory usage while preserving critical model capabilities.
Solution Approach 2:
Different quantization strategies are applied to different blocks based on their individual importance scores. Critical blocks maintain higher precision while less important blocks undergo aggressive quantization, optimizing the balance between model performance and memory efficiency.
2Device complexity
If model parameter quantization is applied to reduce deployment costs, then graphics memory usage is reduced, but the quantization effect deteriorates due to irregular parameter distribution
Solution Approach 1:
The method performs preliminary analysis to calculate importance scores for each block before quantization. This preliminary action identifies which blocks can be safely quantized and which should maintain higher precision, ensuring optimal quantization effects from the outset.
Solution Approach 2:
The method dynamically adjusts quantization parameters (precision levels) for different blocks based on their importance scores. This parameter adaptation allows the system to achieve better quantization effects by allocating precision resources according to actual model needs rather than applying uniform quantization.
3Ease of manufacture
If uniform quantization is applied to all blocks, then the quantization process is simplified, but the model performance loss increases due to irregular parameter distribution
Solution Approach 1:
The method applies different quantization precision levels to different blocks based on their importance. This local differentiation maintains relatively simple quantization procedures while significantly reducing model performance loss compared to uniform quantization.
Solution Approach 2:
The method applies full precision to critical blocks and reduced precision to less important blocks. This partial application of high precision where needed minimizes performance loss while keeping the overall quantization process manageable.
Data Source
AI summary
Embodiments of the present disclosure relate to a quantization method and apparatus for a speech recognition model, an electronic device, and a product. The method comprises determining a weight matrix for a network layer of the speech recognition model, where the weight matrix includes a plurality of blocks divided into a plurality of groups. The method further comprises adjusting an order of the plurality of blocks in the weight matrix according to the plurality of groups and based on a plurality of block parameters of the plurality of blocks, where the block parameter indicates how much a corresponding block affects the speech recognition model. The method further comprises quantizing the plurality of blocks in the adjusted weight matrix according to the adjusted order. In addition, the method further comprises restoring the order of the plurality of blocks in the quantized weight matrix.