FPGA Design Adjustment via ML Error Analysis
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Solution Overview
Problem
The long compilation times for programmable logic devices, such as FPGAs, are computationally and resource intensive, increasing development costs and reducing adoption, and existing error reports are difficult to interpret and resolve.
Innovation Solution
The use of language-based machine learning models to generate prompts that adjust system designs based on compilation errors, mapping error messages to specific design software components, and iteratively refining the design until successful compilation is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fine-grained programmable logic devices are used to implement RTL-based designs, then design flexibility and functionality are improved, but compilation time increases significantly
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict compilation errors before the actual compilation process completes. The system analyzes design code in advance, identifies potential errors, and provides corrections proactively, preventing compilation failures before they occur and significantly reducing iterative compilation time.
2Reliability
If traditional compilation processes are used for programmable logic devices, then comprehensive error detection is achieved, but error interpretation and resolution becomes difficult
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a bridge between the compiler's technical error messages and the designer. The ML model translates complex compilation errors into human-readable explanations with specific line numbers, error types, and suggested fixes, making error resolution accessible to users without deep expertise in compilation processes.
Solution Approach 2:
The system implements self-service by enabling automated error detection and correction suggestions through the machine learning model. The model autonomously analyzes design code, identifies errors, and provides corrective actions without requiring manual intervention from designers, thereby simplifying the error resolution process while maintaining comprehensive error detection.
3Manufacturing precision
If iterative design adjustments are made to resolve compilation errors, then design accuracy is improved, but overall development time increases
Solution Approach 1:
By performing preliminary error analysis using machine learning models before compilation, the system identifies and corrects design errors in advance. This proactive approach prevents the need for multiple iterative compilation-adjustment cycles, achieving high design accuracy while significantly reducing the total development time that would otherwise be spent on repeated iterations.
Data Source
AI summary
Systems or methods of the present disclosure may provide systems and methods for adjusting a system design for a field-programmable gate array (FPGA) in response to a compilation error based on one or more language-based machine learning (ML) models trained on error messages of prior system designs. A method may include receiving an error message associated with a system design of an FPGA, generating a language-based machine learning (ML) prompt based at least on the error message, and determining an adjustment to the system design based on providing the language-based ML prompt to one or more language-based ML models trained on prior error messages.