Neural Network Pruning for Deep Learning Model Optimization
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Solution Overview
Problem
Designing deep learning models with high accuracy and low computation requirements is challenging, as existing Model-Free Reinforcement Learning techniques require substantial memory and computational resources, leading to complex and resource-intensive models.
Innovation Solution
A method involving pruning connections in neural networks based on predetermined policies to generate optimized deep learning models with reduced complexity, while maintaining or enhancing accuracy, by identifying prunable connections, generating intermediate models, and selecting the most accurate ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If Model-Free Reinforcement Learning techniques are used to automatically generate neural networks, then model generation automation is improved, but memory and computational resource requirements increase substantially
Solution Approach 1:
The patent extracts and removes unnecessary connections (weights) from the neural network model. By identifying and eliminating redundant connections through pruning operations, the system reduces memory and computational resource requirements while preserving the essential functionality and accuracy of the model, thus resolving the contradiction between automation and resource consumption.
Solution Approach 2:
The patent applies different pruning strategies and policies to different parts of the neural network. By locally optimizing connection retention based on importance metrics and specific pruning policies applied to different layers or connection types, the system achieves efficient resource utilization while maintaining model performance, addressing the resource consumption issue.
2Manufacturing precision
If deep learning models are designed with high accuracy requirements, then model performance is improved, but device complexity increases
Solution Approach 1:
The patent removes unnecessary connections from the neural network while preserving those critical for high accuracy. By extracting and eliminating redundant weights through systematic pruning, the system reduces model complexity and device requirements while maintaining the accuracy necessary for high-performance applications.
Solution Approach 2:
The patent changes the parameters of the neural network by modifying the number and configuration of connections through pruning. By adjusting the connectivity structure and removing redundant parameters, the system achieves a balance between model accuracy and complexity, reducing device requirements while preserving performance.
3Quantity of substance
If comprehensive pruning of neural network connections is performed, then resource requirements are reduced, but model accuracy may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms to monitor model accuracy during the pruning process. By evaluating performance metrics and using this feedback to guide subsequent pruning decisions, the system can prune connections aggressively when accuracy is maintained and conserve connections when accuracy is at risk, thus reducing resources while preserving accuracy.
Solution Approach 2:
The patent applies partial pruning strategies where not all connections are pruned uniformly. By selectively pruning only the most redundant connections while preserving critical pathways, and using multiple pruning passes with varying policies, the system achieves significant resource reduction without sacrificing model accuracy.
Data Source
AI summary
Provided is a method and system with deep learning model generation. The method includes identifying a plurality of connections in a neural network that is pre-associated with a deep learning model, generating a plurality of pruned neural networks by pruning different sets of one or more of the plurality of connections to respectively generate each of the plurality of pruned neural networks, generating a plurality of intermediate deep learning models by generating a respective intermediate deep learning model corresponding to each of the plurality of pruned neural networks, and selecting one of the plurality of intermediate deep learning models, having a determined greatest accuracy among the plurality of intermediate deep learning models, to be an optimized deep learning model.