Neural Network Pruning via Channel and Weight Segmentation
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Solution Overview
Problem
Pruning methods for neural networks often compromise processing accuracy while aiming to improve computation efficiency, and may not adequately remove redundant parameters, leading to suboptimal performance.
Innovation Solution
A two-stage pruning process is implemented, where the first stage prunes convolutional layers in units of channels and the second stage prunes weight parameters, with layers connected by residual links being pruned differently to maintain accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pruning is performed in units of channels of convolutional layers, then computation efficiency is improved, but processing accuracy drops
Solution Approach 1:
The patent divides the pruning process into two distinct stages: first pruning in units of channels (coarse-grained) to improve computation efficiency, then pruning in units of weight parameters (fine-grained) to preserve processing accuracy. This segmentation allows each stage to focus on specific optimization goals without compromising the other.
Solution Approach 2:
The patent performs preliminary channel-level pruning before weight parameter pruning. By first removing entire channels that contribute least to overall performance, the system prepares the neural network for subsequent more精细 weight pruning, reducing the search space and preventing excessive accuracy loss during the second stage.
2Manufacturing precision
If pruning is performed in units of weight parameters, then processing accuracy is maintained, but computation efficiency improvement is insufficient
Solution Approach 1:
The patent performs preliminary channel-level pruning to remove entire channels that contribute least to overall performance. This preliminary action reduces the number of weight parameters that need to be evaluated in the second stage, making the weight parameter pruning more efficient and enabling greater overall computation efficiency improvement.
Solution Approach 2:
The patent segments the pruning process into two stages where the first stage (channel pruning) performs粗粒度 compression to achieve significant computation efficiency improvement, while the second stage (weight parameter pruning) performs fine-grained optimization to maintain processing accuracy. The combination of both segments resolves the contradiction.
3Productivity
If redundant parameters are extensively deleted, then computation efficiency is improved, but processing accuracy drops significantly
Solution Approach 1:
The patent segments the parameter deletion process into two controlled stages with different deletion granularities. The first stage deletes channels (coarse-grained) with lower impact on accuracy, while the second stage deletes individual weight parameters (fine-grained) with carefully calculated importance scores. This segmentation enables extensive parameter deletion while maintaining processing accuracy through progressive optimization.
Solution Approach 2:
The patent performs preliminary evaluation and classification of channels and weight parameters before deletion. By calculating importance scores and identifying redundant elements in advance, the system can safely delete extensive parameters while preserving those critical for processing accuracy, thus resolving the contradiction between extensive deletion and accuracy maintenance.
Data Source
AI summary
In a method of creating a learning model using a controller configured to perform pruning on a neural network, the pruning includes a first pruning process in which a pruning process is performed in units of channels of convolutional layers and a second pruning process in which a pruning process is performed in units of weight parameters.