Communication-Side AI Model Format Conversion for Deployment Overhead
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems face challenges in adapting AI models trained on different deep learning frameworks due to format incompatibilities, leading to inefficiencies in model deployment and increased computational and air interface overhead.
Innovation Solution
A communication method and device that facilitate model format configuration and conversion between communication devices, enabling adaptive AI model format alignment through model format configuration information exchange and potential format conversion operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are deployed across diverse communication devices with different deep learning frameworks, then model versatility and adaptability improve, but format incompatibility causes deployment inefficiency and increased computational overhead
Solution Approach 1:
The patent introduces a model format configuration mechanism that acts as an intermediary between different deep learning frameworks and communication devices. This configuration system enables format conversion and compatibility adaptation without requiring complete model retraining, thereby resolving the contradiction between supporting diverse frameworks and maintaining deployment efficiency
Solution Approach 2:
The patent changes the parameter of model format representation by introducing configurable format indicators and conversion parameters. Instead of fixing models to specific frameworks, the system allows dynamic parameter adjustment to match different device requirements, improving both versatility and deployment efficiency
2Adaptability or versatility
If model format conversion operations are performed to ensure compatibility, then model adaptability improves, but computational overhead and air interface overhead increase
Solution Approach 1:
The patent performs model format configuration and conversion operations in advance before actual model deployment and execution. By preparing the appropriate model formats beforehand based on device capabilities, the system avoids costly real-time conversion during inference, thereby reducing computational overhead while maintaining format compatibility
Solution Approach 2:
The patent applies partial format conversion only when necessary, rather than converting all models to all possible formats. The system selectively performs conversion based on device capability matching, reducing unnecessary computational overhead while ensuring compatibility where needed
3Adaptability or versatility
If model format configuration information is exchanged between devices, then model compatibility improves, but communication overhead increases
Solution Approach 1:
The patent extracts only the essential model format configuration information needed for compatibility, rather than transmitting complete model data or excessive configuration parameters. This selective extraction reduces air interface overhead while maintaining sufficient information for format alignment and conversion decisions
Data Source
AI summary
The present disclosure relates to a communication method and a device. The method includes a first communication device receiving model format configuration information from a second communication device. The method further includes the first communication device performing a model format-related operation according to the model format configuration information.