AI/ML Model Adjustment Using Deployment Node Capability Feedback
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Solution Overview
Problem
Existing AI/ML models generated by one node may not adapt to the software and hardware environment of the node on which they are deployed, leading to low running efficiency or failure.
Innovation Solution
A method and apparatus for adjusting AI/ML models by considering the capability and environment of the deployment node, involving devices that request, adjust, and deploy the models, ensuring compatibility and improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an AI/ML model is generated by one node and deployed on another node without adjustment, then the model generation process is simple and fast, but the model does not adapt to the software and hardware environment of the deployment node, resulting in low running efficiency or failure
Solution Approach 1:
The patent applies preliminary action by having the deployment node send capability information (software and hardware environment details) to the generation node before model generation. This allows the generation node to pre-adjust the model parameters and structure to match the deployment environment, ensuring the model is ready for efficient execution without requiring post-deployment adjustments.
Solution Approach 2:
The patent implements feedback by using capability information from the deployment node as input to the model generation process. The deployment node's environmental characteristics feed back to the generation node, which then adjusts the model accordingly. This closed-loop information flow ensures the generated model is optimized for the specific deployment environment, resolving the contradiction between simple generation and environmental adaptation.
2Device complexity
If an AI/ML model is adjusted without considering the deployment node's capability information, then the adjustment process is simple, but the adjusted model does not fit the deployment environment, leading to low running efficiency
Solution Approach 1:
The patent applies parameter changes by adjusting model parameters based on capability information from the deployment node. The generation node modifies model parameters (such as precision, structure, or configuration) according to the deployment environment's software and hardware characteristics. This ensures the adjusted model is optimized for the specific deployment node, improving running efficiency while maintaining reasonable adjustment complexity.
3Reliability
If capability information of the deployment node is collected and used for model adjustment, then the model adapts better to the deployment environment, but the information exchange and adjustment process becomes more complex
Solution Approach 1:
The patent applies preliminary action by having the deployment node send its capability information to the generation node before model generation or adjustment. This advance information exchange allows the model to be customized for the deployment environment upfront, ensuring high deployment success rate while keeping the information exchange process simple and structured.
4Adaptability or versatility
If the model is adjusted based on deployment node capability information, then the adaptation to software and hardware environment is improved, but the time required for model adjustment increases
Solution Approach 1:
The patent applies preliminary action by performing model adjustment based on capability information before deployment. By gathering deployment environment information in advance and adjusting the model accordingly, the system achieves high environment adaptability while minimizing adjustment time during actual deployment, as the model is already optimized for the target environment.
Data Source
AI summary
A method for adjusting an artificial intelligence/machine learning (AI/ML) model and an apparatus. A first device sends first information to a second device, where the first information includes information for requesting to adjust a first AI/ML model and/or capability information of a third device. The second device adjusts the first AI/ML model based on the first information, and sends second information. When adjusting the first AI/ML model, the second device can consider an actual case of the device on which the first AI/ML model is deployed, so that an adjustment result can adapt to a software and hardware environment of the third device, to improve adaptation between the AI/ML model and the device on which the AI/ML model is deployed. In this way, execution performance of the AI/ML model can be improved.