Neural Network Compression with Adaptive Layer Quantization
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Solution Overview
Problem
Neural networks require significant computational and storage resources due to numerous layers and parameters, limiting their application on hardware with limited resources, and existing quantization methods cause uneven accuracy loss across different operation layers.
Innovation Solution
Adaptive hybrid quantization is applied to neural networks, where operation layers are compressed with varying quantization accuracies based on sensitivity, using weighting factors to retrain the network and select optimal branches, reducing accuracy loss through forward and backward propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If neural network compression is applied to reduce computational resources and storage needs, then resource consumption is reduced, but network accuracy is compromised
Solution Approach 1:
The patent applies different compression ratios to different operation layers based on their sensitivity to compression. Critical layers with high accuracy requirements maintain lower compression ratios, while less critical layers use higher compression ratios. This local differentiation resolves the contradiction by optimizing resource consumption without uniformly sacrificing accuracy.
Solution Approach 2:
The patent dynamically adjusts compression ratios during the training process through iterative optimization. The compression strategy is not fixed but adapts based on model performance metrics, allowing the system to find the optimal balance between resource consumption and accuracy for each specific task and dataset.
2Device complexity
If uniform compression ratio is applied to all operation layers, then implementation is simplified, but accuracy loss increases due to ignoring layer sensitivity
Solution Approach 1:
The patent introduces layer sensitivity analysis to identify which operation layers are most critical for network accuracy. Different compression ratios are then assigned to different layers based on their sensitivity scores, resolving the contradiction by maintaining simplicity in the compression framework while achieving optimized accuracy through local customization.
Solution Approach 2:
The patent changes the compression parameter (compression ratio) from a uniform value to a variable parameter that depends on layer characteristics. By introducing layer sensitivity as a modulating factor, the system achieves more accurate compression without significantly increasing implementation complexity.
3Quantity of substance
If high compression ratio is applied to maximize storage reduction, then storage needs are minimized, but computational accuracy deteriorates
Solution Approach 1:
The patent applies high compression ratios selectively to operation layers that are less sensitive to compression, while using lower compression ratios for critical layers. This local quality differentiation allows maximum storage reduction without proportionally sacrificing computational accuracy.
Solution Approach 2:
The patent applies compression more aggressively (higher compression ratios) to certain layers where it can tolerate the loss, and less aggressively to other layers. This partial application of high compression achieves overall storage optimization while maintaining sufficient accuracy in critical computational paths.
Data Source
AI summary
A method for compressing a neural network includes: obtaining a neural network including J operation layers; compressing a jth operation layer with Kj compression ratios to generate Kj operation branches; obtaining Kj weighting factors; replacing the jth operation layer with the Kj operation branches weighted by the Kj weighting factors to generate a replacement neural network; performing forward propagation to the replacement neural network, a weighted sum operation being performed on Kj operation results generated by the Kj operation branches with the Kj weighting factors and a result of the weighted sum operation being used as an output of the jth operation layer; performing backward propagation to the replacement neural network, updated values of the Kj weighting factors being calculated based on a model loss; and determining an operation branch corresponding to the maximum value of the updated values of the Kj weighting factors as a compressed jth operation layer.