Neural Network Channel Optimization via Importance-Based Clipping
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Solution Overview
Problem
Existing neural network optimization methods are inefficient due to manual adjustments and potential performance degradation from improper operation replacement, clipping, or pruning, which can lead to reduced accuracy and increased latency.
Innovation Solution
A method that dynamically adjusts the number of channels in operation branches of a neural network by increasing and then clipping channels based on importance measures, ensuring that the extended network block maintains or improves performance while reducing redundant calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of channel distribution is performed by network designer, then network optimization can be achieved, but the process is time-consuming and requires professional expertise
Solution Approach 1:
The system performs self-optimization by automatically determining channel distribution across operation branches using importance measures, eliminating the need for manual designer intervention while maintaining optimization quality
Solution Approach 2:
The system dynamically adjusts channel distribution parameters based on calculated importance measures of operation branches, automatically optimizing network structure without manual intervention
2Productivity
If operation replacement or clipping is performed to optimize network, then network size can be reduced, but network performance may degrade due to improper operation selection
Solution Approach 1:
The system applies different channel distribution strategies to different operation branches based on their specific importance measures, allowing localized optimization that preserves critical operations while reducing redundant ones
Solution Approach 2:
The system calculates importance measures for each operation branch and uses this feedback to dynamically adjust channel distribution, ensuring that operations critical to accuracy receive sufficient channels while non-critical operations are reduced
3Reliability
If channels are increased in operation branches, then network performance can be improved, but total network size increases
Solution Approach 1:
The system changes the distribution parameter of channels across operation branches rather than uniformly increasing total channels, reallocating existing channels to where they provide maximum performance benefit
Solution Approach 2:
The system dynamically adjusts channel distribution based on calculated importance measures, allowing flexible reallocation of channels to match the actual needs of different operation branches
Data Source
AI summary
A method and apparatus with neural network optimization are provided. A method is performed by a device storing a target network block and processing hardware that performs optimizing for the target network block, the method includes generating, by the processing hardware, an extended network block of the target network block by increasing, a number of channels of a target operation branch in the target network block to a determined number of channels, wherein the target network block includes operation branches that include the target operation branch, and wherein each operation branch includes at least one respective channel, determining importance measures of the respective operation branches, including the target operation branch with the increased number of channels, in the extended network block, and clipping a channel of the target operation branch in the extended network block, the clipping is performed according to the importance measures of the respective operation branches.