Pseudocode to RTL Translation via Context Dependency Graph
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Solution Overview
Problem
Current programming tools face challenges in efficiently translating natural language into Register-Transfer Level (RTL) designs, particularly due to complexity and context issues in pseudocode, leading to errors and inefficiencies in code generation.
Innovation Solution
A pseudocode-to-RTL machine translation framework utilizing AI-enabled tools, including a frontend for user interaction, a backend with a natural language parser, Context Dependency Graph (CDG) manager, and a neural engine transformer, which processes pseudocode to generate fully compliant RTL descriptions like Verilog code without human intervention, leveraging context information and hierarchical data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural network text generation models are used to translate pseudocode to HDL, then code generation capability is improved, but accuracy and reliability deteriorate due to context loss and error propagation
Solution Approach 1:
The translation process is segmented into distinct phases: pseudocode parsing to extract context relationships, hierarchical representation in CDG, and staged HDL generation. This segmentation allows each phase to be optimized independently, improving both productivity and reliability by preventing error propagation across the entire translation process.
Solution Approach 2:
A Context Dependency Graph (CDG) is introduced as an intermediary data structure between pseudocode input and HDL output. The CDG preserves context relationships and hierarchical dependencies, acting as a mediator that ensures accurate translation while enabling systematic code generation through structured intermediate representation.
2Adaptability or versatility
If complex pseudocode with nested dependencies is processed, then functionality is improved, but development time increases due to manual intervention requirements
Solution Approach 1:
Context relationships and hierarchical structures are extracted and organized into CDG before HDL generation begins. This preliminary action prepares the translation framework with all necessary context information, enabling automated generation of complex nested dependencies without requiring manual intervention during the actual code generation phase.
Solution Approach 2:
The translation framework performs self-service by automatically parsing pseudocode, extracting context relationships, organizing them in CDG, and generating HDL code without human intervention. This self-service capability handles complex nested dependencies autonomously, significantly reducing development time while maintaining functionality.
3Measurement precision
If context information is extracted and organized in hierarchical structures, then translation accuracy is improved, but system complexity increases
Solution Approach 1:
Context information is organized in an additional hierarchical dimension through the Context Dependency Graph structure. This dimensional organization separates context extraction, storage, and utilization into distinct layers, improving translation accuracy by preserving relationships while managing system complexity through structured abstraction.
Data Source
AI summary
Technology is described for translating natural language to a hardware description language (HDL). The method can include receiving a plurality of pseudocode statements that include natural-language terms for a high-level representation of hardware behavior. Another operation may be extracting context information from the pseudocode statements based in part on context relationships defined in the plurality of pseudocode statements between parent pseudocode statements and inner context pseudocode statements. Lines of pseudocode may be stored in nodes in a context dependency graph, which is ordered using the context relationships from the context information. A pseudocode statement from each node in the context dependency graph can be translated into HDL code modules using a deep neural network. The HDL code modules can be ordered based on the context dependency graph to provide a listing of HDL source code.