Pre-Trained Neural Network Compression With Visual Model Tuning
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Solution Overview
Problem
Existing neural network models face challenges in optimizing performance and accuracy while reducing computational complexity, particularly in resource-constrained environments such as mobile and embedded systems, necessitating efficient compression techniques.
Innovation Solution
A method and system for optimizing pre-trained neural network models through compression techniques like quantization, pruning, and matrix decomposition, accompanied by a graphical user interface for visual comparison and adjustment of model characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network models are pre-trained with high accuracy, then model performance is improved, but model size and computation requirements increase
Solution Approach 1:
The patent extracts and removes redundant or less important components from the pre-trained neural network model through pruning techniques. This involves identifying and eliminating unnecessary neurons, connections, or filters that contribute minimally to model performance, thereby reducing model size while preserving accuracy.
Solution Approach 2:
The patent applies quantization to change the precision parameters of model weights from high-precision formats (e.g., 32-bit floating point) to lower-precision formats (e.g., 8-bit integers). This parameter transformation reduces memory storage requirements and computation complexity while maintaining acceptable model performance.
2Reliability
If neural network models are pre-trained with high accuracy, then model performance is improved, but computation requirements increase
Solution Approach 1:
The patent removes computationally expensive components or operations from the model through pruning. By eliminating redundant neurons and connections, the overall computation load is reduced, making the model more suitable for deployment on resource-constrained devices with limited processing power.
Solution Approach 2:
The patent transforms weight precision parameters to reduce computation requirements. By converting weights from 32-bit floating-point to 8-bit integer representations, arithmetic operations become less computationally intensive and can be executed more efficiently on mobile and embedded processors.
3Weight of stationary object
If model compression is applied to reduce size, then model size is reduced, but model performance may deteriorate
Solution Approach 1:
The patent performs preliminary training of the neural network model to achieve high accuracy before applying compression techniques. This pre-training ensures that the model learns optimal weight distributions that are more resilient to subsequent pruning and quantization operations, helping maintain performance after compression.
Solution Approach 2:
The patent employs iterative optimization where compression is applied, performance is evaluated, and the model is retrained or fine-tuned based on performance feedback. This closed-loop process allows the model to adapt to compression-induced degradation and recover performance while maintaining reduced size.
4Quantity of substance
If complex compression techniques are applied, then compression ratio is improved, but device complexity increases
Solution Approach 1:
The patent divides the model compression process into distinct sequential stages: pruning to remove redundant components, followed by quantization to reduce precision, and finally fine-tuning to recover performance. This segmentation allows each technique to be applied independently and systematically, managing overall process complexity.
Data Source
AI summary
A method of optimizing a neural network model includes receiving original model information about a first neural network model that is pre-trained; generating a second neural network model and compressed model information about the second neural network model by performing a compression on the first neural network model; and outputting, on a screen, at least a part of the original model information and at least a part of the compressed model information.