Network Structure Optimizer for Neural Network Redundancy Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural network structures often contain redundant calculation units or operations, leading to increased calculation costs and reduced model performance, particularly in resource-constrained environments.
Innovation Solution
A network structure optimizer is employed to perform feature extraction, predict optimization manners, and update the network structure, removing redundant operations and optimizing the architecture without additional calculation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the network structure contains more calculation units to improve model performance, then the model performance is improved, but the calculation costs increase
Solution Approach 1:
The patent extracts and removes redundant calculation units from the neural network structure through automated optimization. The network structure optimizer identifies and eliminates unnecessary calculation operations while preserving the essential computational pathways needed for image recognition performance, thereby reducing calculation costs without sacrificing model effectiveness
Solution Approach 2:
The patent changes the structural parameters of the neural network by adjusting the configuration, architecture, and connectivity patterns. The optimizer modifies network parameters such as layer depths, filter sizes, and connection patterns to achieve an optimal balance between computational efficiency and performance, transforming the network into a more efficient structure
2Reliability
If manual optimization of network structure is performed, then the network structure can be optimized, but the complexity and time consumption increase
Solution Approach 1:
The patent implements self-service optimization where the network structure optimizer automatically analyzes, evaluates, and optimizes the neural network architecture without requiring manual intervention. The system self-evaluates performance metrics, self-identifies redundant components, and self-adjusts the network structure, eliminating the complexity and time consumption associated with manual optimization processes
Solution Approach 2:
The patent incorporates feedback mechanisms where the optimizer continuously monitors performance parameters, compares optimized structures against original configurations, and uses this feedback to iteratively improve the network architecture. This automated feedback loop enables continuous optimization without manual review, reducing overall complexity while maintaining optimization effectiveness
Data Source
AI summary
This application provides a method for constructing a network structure optimizer performed by an electronic device. The method includes: performing feature extraction on a network structure of an image recognition neural network by using a network structure optimizer, to obtain feature information corresponding to the network structure; predicting the feature information by using the network structure optimizer, to determine a plurality of optimization manners for the network structure; updating the network structure of the image recognition neural network according to the optimization manners for the network structure, to obtain an optimized network structure of the image recognition neural network; and determining an image recognition performance parameter of the optimized network structure of the image recognition neural network, and updating a parameter of the network structure optimizer according to the image recognition performance parameter, the network structure optimizer being configured to optimize the network structure of the image recognition neural network.