Model Acceleration via User Data Adaptation
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Solution Overview
Problem
Deep learning model deployment on mobile devices faces challenges due to increased model size, storage requirements, and inference latency, necessitating the development of model compression and acceleration techniques.
Innovation Solution
A method that determines a target acceleration strategy based on model-specific parameters and user data sets, processing the model to adapt to user-specific data distribution and characteristics, utilizing multi-framework and multi-heterogeneous hardware to create a lightweight, efficient, and adaptable model for various hardware and application scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning models are deployed on mobile devices, then AI applications can be realized on mobile devices, but model size increases leading to higher storage requirements and computing resource consumption
Solution Approach 1:
The patent applies parameter changes by transforming model parameters through quantization (converting to lower precision formats like INT8), pruning (removing redundant parameters), and compression techniques. These parameter transformations reduce the model size while maintaining computational functionality, directly addressing the contradiction between AI capability and model size requirements on mobile devices
Solution Approach 2:
The patent extracts and removes redundant or less important parameters from the model through pruning techniques. By identifying and extracting unnecessary computational elements, the model achieves reduced size and lower storage requirements while preserving the core AI processing capabilities needed for mobile deployment
2Adaptability or versatility
If model size increases to handle complex AI tasks, then AI functionality improves, but inference latency increases failing to meet ideal requirements
Solution Approach 1:
The patent changes computational parameters by optimizing data types, precision levels, and calculation methods. Through quantization and parameter transformation, the model achieves faster inference speeds while maintaining accuracy, directly reducing the latency problem associated with larger models
Solution Approach 2:
The patent substitutes traditional computational mechanisms with optimized algorithms and hardware-accelerated operations. By replacing standard processing with specialized inference engines and optimized computational graphs, the system achieves reduced inference latency without sacrificing model capability
3Quantity of substance
If general model compression techniques are applied, then model size is reduced, but the model fails to adapt to user-specific data distribution and characteristics
Solution Approach 1:
The patent applies local quality by customizing model compression and optimization parameters specific to each user's data distribution characteristics. Instead of applying uniform compression, the system adjusts quantization levels, pruning ratios, and optimization strategies according to the specific data characteristics of each user, enabling both size reduction and personalized adaptation
Solution Approach 2:
The patent introduces dynamic adaptation where the model compression strategy is not fixed but adjusts based on user data characteristics. The system dynamically selects and optimizes compression parameters according to the specific data distribution of each user, enabling the model to adapt to individual needs while maintaining reduced size
Data Source
AI summary
The disclosure provides a method for accelerating a model, an apparatus for accelerating a model, a device and a medium, and relates to a technical field of artificial intelligence, in particular to technical fields of deep learning and cloud service. The method includes: obtaining a model to be accelerated and acceleration-related parameters corresponding to the model to be accelerated; determining a target acceleration strategy corresponding to the model to be accelerated according to the acceleration-related parameters; determining whether there is a dependency between the model to be accelerated and a user data set; and in response to there being a dependency between the model to be accelerated and the user data set, obtaining a target user data set sent by a user, and obtaining a target model by processing the model to be accelerated based on the target user data set and the target acceleration strategy. The disclosure enables the model to better adapt to user-specific data distribution and characteristics to improve the generalization ability and accuracy of the model, so that a final deployed model is more lightweight, efficient, and adaptable to various hardware and application scenarios, therefore reducing cost and complexity of model deployment.