Dynamic FPGA Design Scoping for Accurate AI File Search
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Solution Overview
Problem
As programmable logic devices become more complex, existing design tools often provide unintelligible or erroneous results, complicating the design process for integrated circuits.
Innovation Solution
A design assistant tool, such as QuartusPilot, provides a graphical user interface and AI-assisted design analysis to identify errors, suggest improvements, and optimize designs for field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If design tools are used for complex programmable logic devices, then design assistance is provided, but the results become unintelligible or erroneous
Solution Approach 1:
The patent segments the design space into hierarchical scopes (project scope, file scope, function scope) with scope boundaries that separate different design contexts. This segmentation allows the tool to analyze and present results at appropriate granularities, preventing information overload and improving result intelligibility while maintaining comprehensive design assistance.
Solution Approach 2:
The patent applies local quality by implementing scope-specific analysis and presentation rules. Different scopes receive tailored analysis depth and result formatting - for example, project-level summaries provide high-level overview while file-level analysis provides detailed insights. This localized approach ensures results are appropriately detailed for each context, improving both ease of operation and result accuracy.
2Adaptability or versatility
If the design scope is expanded to cover more resources, then more comprehensive analysis is achieved, but the complexity of managing design files increases
Solution Approach 1:
The patent implements dynamic scope management where the analysis scope can be flexibly adjusted based on user needs and design context. The system dynamically determines which files and resources to include in analysis, allowing comprehensive coverage when needed while simplifying management when the full scope is not required. This dynamic approach resolves the contradiction between comprehensive coverage and management complexity.
Solution Approach 2:
The patent introduces scope boundaries as intermediary constructs that mediate between the entire design space and specific analysis targets. These boundaries act as filters and organizers, allowing the system to manage large numbers of design files by grouping them into scoped units with defined relationships. This intermediary layer simplifies file management while maintaining the ability to perform comprehensive analysis across multiple scopes.
3Reliability
If AI-assisted analysis is applied to the entire design, then more errors are detected, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by performing scope boundary identification and file classification before the main AI analysis. The system pre-processes the design to establish scoped hierarchies and identify relevant files for each scope, so that when AI analysis runs, it can focus on specific scoped regions rather than brute-forcing through the entire design. This preliminary organization enables thorough error detection while reducing processing time.
Solution Approach 2:
The patent implements partial action by applying AI analysis selectively to scoped regions rather than uniformly to the entire design. The system identifies high-priority scopes for detailed analysis and may apply different analysis depths to different scopes based on their importance and complexity. This selective approach maintains high error detection capability for critical areas while reducing overall processing time.
Data Source
AI summary
Systems or methods of the present disclosure may provide a design tool for adjusting designs implemented on programmable logic devices. The present disclosure includes receiving a query from a user and processing commands provided by the user associated with the query. The present disclosure also includes providing the query and the commands to a large language model (LLM), selecting one or more relevant file types based on the query, and executing searches scoped to the one or more relevant file types and filtering out irrelevant information via the LLM. Furthermore, the present disclosure includes receiving a set of results from the LLM and filtering and ranking the set of results based on relevance to the query.