Cross-Platform Model Optimization Using Device-Aware Training
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Solution Overview
Problem
Current model deployment technologies are time-consuming, error-prone, and face challenges in cross-platform deployment due to significant performance and resource disparities among devices, requiring manual adjustments that are inefficient and prone to errors.
Innovation Solution
A model optimization method involving automated strategies such as model weight adjustment, dynamic quantization, operator lists, and resource assessment-based optimization to optimize model performance across different devices, including transformation functions, dynamic quantization, and lightweight deployment methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment and optimization of models is performed during deployment, then model performance can be tailored to specific devices, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent applies preliminary action by pre-processing the training dataset with transformation functions that represent target device processing capabilities before model training. This prepares the model in advance for specific device constraints, eliminating the need for time-consuming manual adjustments during deployment while maintaining optimized performance for the target device
Solution Approach 2:
The system implements self-service through automated model optimization that performs dataset transformation, model training, and performance evaluation without manual intervention. The automated workflow adjusts models according to device capabilities independently, reducing both deployment time and human error while maintaining manufacturing precision
2Adaptability or versatility
If traditional model deployment methods are used across different devices, then deployment can be performed, but significant performance and resource disparities among devices cause numerous challenges
Solution Approach 1:
The patent applies local quality by using device-specific transformation functions that represent the processing capabilities of each target device. These transformation functions are applied to the training dataset to create device-tailored versions, ensuring each model is optimized for its specific deployment environment's local characteristics rather than using a universal approach
Solution Approach 2:
The system implements parameter changes by modifying the training dataset through transformation functions that encode device capability parameters. These transformations adjust data characteristics to match target device constraints, enabling reliable cross-platform deployment while maintaining performance consistency across different hardware configurations
3Productivity
If model deployment is performed without optimization, then deployment speed is fast, but performance degradation and resource inefficiencies occur
Solution Approach 1:
The patent applies preliminary action by performing dataset transformation and model optimization before deployment. This pre-processing ensures the model is already optimized for the target device's processing capabilities, allowing fast deployment without subsequent performance degradation or resource inefficiencies
Solution Approach 2:
The system implements feedback through automated performance evaluation that measures model metrics on target devices and uses this information to guide further optimization. This feedback loop ensures deployment speed is maintained while preventing performance degradation by continuously validating model behavior against device-specific criteria
Data Source
AI summary
The present disclosure provides a model optimization method capable of implementing cross-platform intelligent model deployment. A model optimization method executed on a first device may include: performing training using a first training dataset to obtain a first model; transforming the first training dataset with a transformation function to obtain a transformed dataset, and generating a second training dataset based on the first training dataset and the transformed dataset; training the first model using the second training dataset to obtain an adjusted first model; performing performance tests on the first model and the adjusted first model using a test dataset to respectively obtain a first performance metric and a second performance metric, and calculating a performance metric difference between the first and second performance metrics; generating an adjusted second training dataset based on the performance metric difference.