Bayesian Optimization for Neural Network Filter Pruning
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Solution Overview
Problem
Existing filter pruning methods in neural networks apply the same pruning rates to all convolutional layers, which is inefficient and does not account for the varying importance of filters across different layers, leading to suboptimal model compression and accuracy degradation.
Innovation Solution
A method using Bayesian optimization to determine a combination of optimal pruning rates for each convolutional layer based on a novel objective function that measures accuracy degradation, allowing for adaptive pruning rates tailored to each layer, referred to as High-Dimensional Bayesian Optimization-based Filter Pruning (HDBOFP).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same pruning rate is applied to all convolutional layers, then the pruning process is simple and fast, but the accuracy degradation is high and model compression is suboptimal
Solution Approach 1:
The patent applies different pruning rates to different convolutional layers based on their specific characteristics and importance. Each layer is evaluated individually and assigned a tailored pruning rate, ensuring that critical layers retain more filters while less important layers can be more aggressively pruned, thus maintaining overall model accuracy while achieving effective compression.
Solution Approach 2:
The patent introduces a dynamic pruning rate determination mechanism that adapts to the specific needs of each convolutional layer. Rather than using a static uniform pruning rate, the system dynamically calculates optimal pruning rates based on layer-wise importance metrics, allowing the pruning strategy to respond to the actual structure and function of the neural network.
2Ease of manufacture
If manual specification of pruning rates is used, then the process is simple to implement, but it does not account for varying filter importance across layers leading to suboptimal compression
Solution Approach 1:
The patent enables the neural network to determine its own optimal pruning rates automatically through a systematic evaluation process. The method calculates importance metrics for each layer and derives pruning rates based on these metrics, allowing the model to self-optimize its compression without requiring manual intervention or expert knowledge, thus achieving both simplicity and effectiveness.
Solution Approach 2:
The patent systematically varies and optimizes the pruning rate parameter for each convolutional layer based on calculated importance metrics. By changing the pruning rate parameter dynamically according to layer-specific characteristics, the method achieves optimal model compression while maintaining performance, moving beyond fixed manual specifications.
3Quantity of substance
If aggressive pruning is applied to achieve high compression, then model size and computational cost are reduced, but accuracy degradation increases
Solution Approach 1:
The patent prevents accuracy degradation by applying differentiated pruning rates to different layers. Critical layers that contribute more to model accuracy are assigned lower pruning rates to preserve their filtering capacity, while less important layers receive higher pruning rates. This local quality approach ensures aggressive compression where safe and conservative pruning where necessary, maintaining overall accuracy while achieving high compression ratios.
Data Source
AI summary
Provided is a method of pruning a plurality of convolutional layers in a target neural network. The method includes: acquiring the target neural network including the plurality of convolutional layers; setting a condition of an objective function on the basis of combinations of pruning rates respectively applied to the plurality of convolutional layers, wherein the condition is that the combination of pruning rates minimizing a value of the objective function minimizes a difference between filters of the plurality of convolutional layers and filters of the plurality of convolutional layers pruned by the combination of pruning rates minimizing the value of the objective function; and determining the combination of pruning rates minimizing the value of the objective function as a combination of optimal pruning rates from the objective function on the basis of Bayesian optimization.