Gradient-Free Structured Pruning for Neural Network Filter Reduction
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Solution Overview
Problem
The increasing complexity and size of neural networks to achieve higher accuracy lead to significant computational costs, making them impractical for deployment on devices with limited resources such as mobile computing devices, where memory and processing capabilities are constrained.
Innovation Solution
A neural network architecture pruning system that uses gradient-free structured pruning techniques to reduce the model size and complexity by pruning filters in the network, allowing the pruned network to maintain high accuracy while fitting within resource-limited devices without the need for retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network is made larger and more complicated to achieve higher accuracy, then the accuracy is improved, but the computational cost increases
Solution Approach 1:
The patent extracts and removes redundant or less important filters from the neural network layers through pruning. By identifying and eliminating unnecessary filters that contribute minimally to the overall accuracy, the model size and computational cost are reduced while preserving the essential functionality and accuracy of the network.
Solution Approach 2:
The patent changes the parameters of the neural network by adjusting which filters are retained or removed. Through gradient-free optimization, the importance of each filter is evaluated and parameters are modified to keep only the most significant filters, thereby reducing model complexity and computational requirements while maintaining accuracy.
2Measurement precision
If the neural network is made larger and more complicated to achieve higher accuracy, then the accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent extracts and removes redundant or less important filters from the neural network layers through pruning. By identifying and eliminating unnecessary filters that contribute minimally to the overall accuracy, the model size and computational cost are reduced while preserving the essential functionality and accuracy of the network.
Solution Approach 2:
The patent changes the parameters of the neural network by adjusting which filters are retained or removed. Through gradient-free optimization, the importance of each filter is evaluated and parameters are modified to keep only the most significant filters, thereby reducing model complexity and computational requirements while maintaining accuracy.
3Use of energy by stationary object
If filters are pruned from the neural network to reduce model size, then the resource consumption is reduced, but the accuracy may be lost
Solution Approach 1:
The patent employs gradient-free optimization with feedback mechanisms to evaluate the importance of each filter. By computing importance metrics based on the network's performance and filter contributions, the system provides feedback to determine which filters can be safely removed without significantly impacting accuracy, thus enabling informed pruning decisions.
Solution Approach 2:
The patent changes the parameters of the neural network by adjusting which filters are retained or removed. Through gradient-free optimization, the importance of each filter is evaluated and parameters are modified to keep only the most significant filters, thereby reducing model complexity and computational requirements while maintaining accuracy.
4Device complexity
If traditional pruning techniques are used to reduce model size, then the device complexity is reduced, but retraining is required which increases time and resource consumption
Solution Approach 1:
The patent extracts and removes redundant or less important filters from the neural network layers through pruning. By identifying and eliminating unnecessary filters that contribute minimally to the overall accuracy, the model size and computational cost are reduced while preserving the essential functionality and accuracy of the network.
Solution Approach 2:
The patent changes the parameters of the neural network by adjusting which filters are retained or removed. Through gradient-free optimization, the importance of each filter is evaluated and parameters are modified to keep only the most significant filters, thereby reducing model complexity and computational requirements while maintaining accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a machine learning task on a network input to generate a network output. One of the methods includes: obtaining data specifying an initial neural network configured to perform a machine learning task; a representativeness measure for each of a plurality of filters; determining a central tendency measure for the plurality of filters based on processing a batch of network inputs using the initial neural network; determining a cumulative importance score for each of the plurality of filters; selecting a proper subset of the plurality of filters; and generating a pruned neural network configured to perform the machine learning task.