Neural Network Model Optimization via Suitability Analysis Algorithms
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Solution Overview
Problem
Existing neural network models lack efficiency in determining suitability for target devices, leading to suboptimal performance and resource utilization due to the lack of effective optimization techniques.
Innovation Solution
A method and system for optimizing neural network models by receiving model and device information, using suitability determination algorithms to analyze and visualize performance, complexity, and memory efficiency, allowing for layer modifications and quantization scheme changes through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional general-purpose optimization algorithms are used for neural network models, then the model can be optimized to some extent, but the optimization efficiency and suitability for specific target devices are insufficient
Solution Approach 1:
The patent segments the optimization process into multiple independent algorithms, each targeting specific aspects: structure analysis algorithm for model architecture, complexity analysis algorithm for computational complexity, and memory analysis algorithm for memory usage. This segmentation allows each algorithm to specialize in one dimension, improving overall optimization efficiency and device suitability.
Solution Approach 2:
The patent changes the parameters being optimized by introducing multiple analysis dimensions (structure, complexity, memory) with different evaluation metrics. Instead of using a single general-purpose optimization approach, the system adjusts and optimizes multiple parameters simultaneously, enabling better adaptation to specific target devices with varying resource constraints.
2Reliability
If the neural network model is designed to be highly accurate, then the model performance improves, but the model complexity and resource requirements increase
Solution Approach 1:
The patent applies local quality by providing detailed, layer-specific analysis results that identify which particular layers or components contribute most to complexity. This allows targeted optimization of specific model portions rather than uniform simplification, maintaining accuracy in critical areas while reducing complexity elsewhere.
Solution Approach 2:
The system implements feedback by analyzing the neural network model and providing optimization suggestions based on the analysis results. The feedback loop allows users to review the complexity and accuracy trade-offs, then adjust the model accordingly, achieving balanced optimization between model accuracy and complexity.
3Measurement precision
If comprehensive analysis algorithms are used to evaluate model suitability, then the optimization quality improves, but the analysis time and computational overhead increase
Solution Approach 1:
The patent divides the comprehensive analysis into three separate, specialized algorithms (structure analysis, complexity analysis, memory analysis) that can be executed independently and in parallel. This segmentation reduces the overall analysis time compared to a single monolithic analysis approach, while maintaining comprehensive evaluation coverage.
Solution Approach 2:
The system implements partial action by allowing users to select which analysis algorithms to execute based on their specific needs. Not all analyses need to be run in every case - users can choose only the relevant analyses for their target device and optimization goals, reducing unnecessary computational overhead while maintaining precision where needed.
Data Source
AI summary
In a method of optimizing a neural network model, first model information about a first neural network model is received. Device information about a first target device that is used to execute the first neural network model is received. An analysis whether the first neural network model is suitable for executing on the first target device is performed, based on the first model information, the device information, and at least one of a plurality of suitability determination algorithms. A result of the analysis is output such that the first model information and the result of the analysis are displayed on a screen.