AI Model Conversion via Master Table Intermediary
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Solution Overview
Problem
Current methods for converting artificial intelligence models from one framework to another are labor-intensive and require manual framework-specific pre-processing and post-processing, increasing system complexity and memory requirements.
Innovation Solution
A computer-implemented method using a master table to identify framework-specific configurations and convert AI models into a uniform exchange formatted model, allowing for intermediate inferencing without further conversion to LLVM IR, thereby reducing complexity and memory needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual framework-specific pre-processing and post-processing methods are used for AI model conversion, then conversion accuracy can be maintained, but system complexity and memory requirements increase
Solution Approach 1:
The patent introduces an intermediate representation (IR) format as a mediator between different AI frameworks. The conversion process uses framework-specific pre-processing to convert source framework models to the intermediate representation, then applies framework-specific post-processing to convert the intermediate representation to the target framework. This intermediary format standardizes the conversion process and reduces system complexity by decoupling framework-specific operations from the core conversion logic.
Solution Approach 2:
The conversion process is segmented into distinct phases: pre-processing, intermediate representation generation, and post-processing. Each phase handles specific tasks independently, allowing the system to manage complexity through modular organization while maintaining conversion accuracy through specialized handling of framework-specific requirements at each stage.
2Adaptability or versatility
If traditional conversion methods involving LLVM IR are used, then comprehensive framework support is achieved, but memory requirements increase
Solution Approach 1:
The patent uses an optimized intermediate representation that serves as a lightweight mediator between frameworks. This intermediate format captures essential model information without the overhead of traditional LLVM IR, enabling comprehensive framework support while reducing memory consumption through more efficient data structures and representation.
Solution Approach 2:
The patent changes the parameters of the intermediate representation to optimize memory usage. By modifying how model data is structured and stored in the intermediate format, the system achieves the same framework compatibility goals with reduced memory requirements compared to traditional LLVM IR-based approaches.
3Adaptability or versatility
If multiple conversion tools and frameworks are integrated, then broad AI model support is achieved, but ease of operation decreases
Solution Approach 1:
The patent creates a universal conversion tool that supports multiple AI frameworks through a single unified interface. The system implements framework-specific pre-processing and post-processing capabilities within one tool, eliminating the need for users to manage multiple separate conversion utilities while maintaining broad model format support.
Solution Approach 2:
The standardized intermediate representation serves as a universal mediator that enables a single conversion tool to handle multiple frameworks. By routing all conversions through this common intermediate format, the system provides broad framework support through one unified tool rather than requiring separate tools for each framework pair.
Data Source
AI summary
Aspects of the invention include converting an artificial intelligence (AI) model generated in a first framework to a uniform exchange formatted model by engaging a master table to retrieve instructions for converting from the AI model to the uniform exchange formatted model in accordance with the first framework. The uniform exchange formatted model in compiled by engaging the master table to retrieve instructions for compiling the uniform exchange formatted model in accordance with the first framework. Data is received as an input to the compiled uniform exchange formatted model and an output is generated by engaging the master table to retrieve instructions for generating the output in accordance with the first framework.