Dynamic Quantization Parameter Adjustment in Recurrent Neural Networks
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Solution Overview
Problem
Traditional recurrent neural networks face challenges in data processing efficiency and storage capacity due to increasing data volume and complexity, leading to low precision in computation results due to fixed bit width quantization methods that apply the same quantization parameters across all data.
Innovation Solution
A method and apparatus for adjusting quantization parameters in recurrent neural networks by determining a target iteration interval based on data variation ranges, allowing for dynamic adjustment of quantization parameters to improve precision and reliability of computation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If fixed bit width quantization is applied to recurrent neural network data, then storage capacity and memory access efficiency are improved, but manufacturing precision (quantization precision) deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from fixed quantization parameters to dynamic adjustment. The system determines data variation ranges during different iteration intervals and adjusts quantization parameters accordingly, allowing the quantization precision to adapt to changing data characteristics while maintaining efficient storage utilization.
Solution Approach 2:
The patent implements parameter changes by modifying quantization parameters based on observed data variation ranges. The system calculates statistical properties of data within specific iteration intervals and uses these parameters to dynamically adjust the quantization configuration, thereby optimizing both precision and storage efficiency.
2Device complexity
If the same quantization parameters are applied to all computation data in recurrent neural network, then device complexity is reduced, but manufacturing precision (quantization precision) deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the recurrent neural network computation process into distinct iteration intervals. Each interval is analyzed separately to determine its specific data variation range, allowing different quantization parameters to be applied to different segments of the computation process, thereby improving precision without excessive complexity.
Solution Approach 2:
The patent implements local quality by tailoring quantization parameters to the specific characteristics of each iteration interval's data. Rather than applying uniform parameters globally, the system determines appropriate quantization settings for each local segment based on its data distribution, achieving higher overall precision with manageable complexity.
3Manufacturing precision
If data variation range is analyzed and target iteration interval is determined to adjust quantization parameters, then manufacturing precision (quantization precision) is improved, but device complexity increases
Solution Approach 1:
The patent applies periodic action by adjusting quantization parameters at regular iteration intervals rather than continuously. The system determines a target iteration interval and performs quantization parameter adjustments periodically at these intervals, reducing the computational overhead and system complexity while maintaining improved quantization precision throughout the computation process.
Data Source
AI summary
A method for adjusting quantization parameters of a recurrent neural network according to an embodiment of the present disclosure may determine a target iteration interval according to the data variation range of the data to be quantized to adjust quantization parameters in the recurrent neural network computation according to the target iteration interval. The quantization parameter adjustment method, apparatus, and related products of the recurrent neural network of the present disclosure may improve the quantization precision, efficiency, and computation efficiency of the recurrent neural network.