Deep Learning Model Conversion Optimization Through Weight Analysis

Solution Overview

Problem

Current deep learning frameworks face inefficiencies in model conversion processes, requiring professional engineer intervention and being time-consuming, which hinders optimal prediction accuracy.

Innovation Solution

A device and method for optimizing deep learning model conversion by analyzing weighting arrangements and quantization results across different frameworks, generating optimization suggestions to improve prediction accuracy and resource efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If model conversion is performed manually by experienced engineers, then prediction accuracy can be optimized, but the process is time-consuming and inefficient

Engineering Contradiction:

Improveprediction accuracyVSAvoidconversion time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-optimization by automatically analyzing conversion logs, comparing model outputs, and generating optimization suggestions without requiring manual intervention from experienced engineers. The automated analysis device executes the optimization process independently, eliminating the time-consuming manual workflow while maintaining accuracy optimization capabilities

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of engineer analysis and optimization is replaced with an automated computational system. The analysis device uses algorithms to automatically compare Tensorflow and NCNN model outputs, analyze conversion logs, and generate optimization suggestions, substituting human engineering expertise with automated mechanical/computational processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If manual analysis and optimization is performed, then model conversion quality can be ensured, but the process relies on professional expertise and is inefficient

Engineering Contradiction:

Improvemodel conversion qualityVSAvoidconversion efficiency

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system performs self-optimization by automatically analyzing conversion logs, comparing model outputs, and generating optimization suggestions without requiring manual intervention from experienced engineers. The automated analysis device executes the optimization process independently, eliminating the time-consuming manual workflow while maintaining accuracy optimization capabilities

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of engineer analysis and optimization is replaced with an automated computational system. The analysis device uses algorithms to automatically compare Tensorflow and NCNN model outputs, analyze conversion logs, and generate optimization suggestions, substituting human engineering expertise with automated mechanical/computational processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated optimization is implemented, then processing efficiency is improved, but computational resources are required

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidcomputational resources

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system extracts only the essential information needed for optimization by analyzing conversion logs and comparing model outputs selectively. Rather than processing entire models, it focuses on extracting key differences and optimization opportunities from the conversion process data, reducing unnecessary computational overhead while maintaining optimization effectiveness

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12367429B2Method and device for optimizing deep learning model conversion, and storage medium

Publication Date: 2025.07.22 HON HAI PRECISION INDUSTRY CO LTD

AI summary

A method for optimizing the conversion of a deep learning model to process other data, applied in a device, includes converting a first deep learning model to obtain a second deep learning model, obtaining a weighting arrangement of the two models according to their deep learning frameworks and performing a quantization on the two models. A similarity in weighting between the two models is analyzed to produce a weighting analysis based on the first and second weighting arrangement and the first and second model quantization result weighting. The two models are tested to establish a model performance analysis. One or more suggestions for optimization are obtained based on the weighting analysis and the model performance analysis, and are applied to optimize the second deep learning model, an optimized second deep learning model being employed to process the other data.