HDL Code Generation Using Non-Textual LLM Fine-Tuning
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Solution Overview
Problem
Conventional large language models (LLMs) struggle to effectively generate and verify hardware description language (HDL) code due to the scarcity of training data and the complexity of non-textual representations, limiting their ability to learn diverse and complex coding patterns, and the inherent difficulty in verifying HDL code correctness.
Innovation Solution
Fine-tuning a pretrained LLM using synthetic training data generated from non-textual representations such as Karnaugh Maps, truth tables, finite state machines, and waveforms, along with targeted code repair data to enhance the model's ability to handle non-textual inputs and improve HDL code generation and verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional LLMs are used for HDL code generation, then the model can handle general programming tasks, but it fails to effectively learn diverse and complex coding patterns associated with HDLs due to scarcity of training data
Solution Approach 1:
The patent applies preliminary action by pre-processing natural language requirements into structured intermediate representations (such as abstract syntax trees or design specifications) before generating HDL code. This preliminary transformation creates a more reliable generation pathway that compensates for the scarcity of training data, as the model learns to map structured intermediates to HDL code rather than directly from unstructured text.
Solution Approach 2:
The patent introduces an intermediary representation layer between natural language input and HDL code output. This intermediary (such as a structured design model or intermediate code representation) acts as a mediator that bridges the gap caused by limited training data, allowing the model to learn complex HDL patterns through the structured intermediate format rather than requiring extensive direct text-to-code training pairs.
2Productivity
If LLMs are trained to generate HDL code, then code generation capability is improved, but verification of correctness becomes inherently more complex compared to software code
Solution Approach 1:
The patent applies preliminary action by performing formal verification and correctness checking on intermediate representations before final HDL code generation. This early verification step catches errors in the logical structure before they propagate to the final code, making the verification process more manageable by breaking it into smaller, verifiable stages rather than verifying the complete HDL code at once.
Solution Approach 2:
The patent segments the HDL code generation and verification process into multiple independent stages: natural language interpretation, intermediate representation generation, structural verification, and final code synthesis. Each segment can be verified independently, reducing the overall complexity of correctness verification compared to treating the entire generation process as a single unverifiable step.
3Reliability
If more training data is collected to improve HDL code generation, then model performance improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent uses copying by creating synthetic training data through transformation of existing HDL code and design specifications. Instead of collecting diverse real-world HDL code samples (which would require complex data processing systems), the system generates synthetic training pairs by copying and transforming existing verified code, maintaining high quality while avoiding the complexity of large-scale data collection and validation infrastructure.
Data Source
AI summary
Embodiments of the present disclosure provide systems and methods for fine-tuning a pretrained large language model (LLM) for generating hardware description language (HDL) code. In at least one embodiment, a first training dataset that includes correct-by-construction non-textual representation data samples is obtained, and the pretrained LLM is fine-tuned using the first training dataset to provide the fine-tuned LLM for generating HDL code.