LLM-assisted Bug Identification and Correction for Verilog HDL

As technology continues to advance, it becomes increasingly integrated into daily life facilitating complex tasks across a range of environments. While some applications such as smartphones and smartwatches are less critical, others like healthcare devices and autonomous vehicles demand bug-free performance to prevent financial loss or harm. Traditionally, simulation-based testing and formal verification played a major role in ensuring a bug-free device. However, the simulation of bigger systems is limited to a definite number of scenarios on the Design under Verification (DUV). Hence, it is unable to explore all possible inputs that can occur. Formal verification, on the other hand, offers a higher level of assurance through mathematical proofs but is both time-consuming and suffers from scalability issues, especially as designs grow in complexity. Recently, Large Language Models (LLMs) have shown promise in tasks previously limited to human expertise. Their natural language processing capabilities can assist in handling extensive specifications and source code, particularly in debugging hardware descriptions and analyzing security and functionality. The utilization of Retrieval Augmented Generation (RAG) has further enhanced LLMs by incorporating large specification or source code bases, thereby improving their bug-identification and correction capabilities. While recent advancements in LLMs, particularly with RAG, have yielded promising results in bug identification and correction for a small class of hardware bugs, significant gaps remain in their full potential for systematically addressing a wide range of hardware bugs. For instance, existing LLM methodologies struggle to detect bugs involving incorrect constant values, i.e., the use of wrong constants in source code. This limitation underscores the need for further exploration in utilizing LLMs to fully optimize the verification process. To bridge this gap, we propose a 3-phased 4-stage LLM-assisted systematic bug closure methodology that focuses on functional bugs in Verilog HDL rather than structural or syntactic issues. Our approach extracts functional properties of the DUV and systematically breaks down complex expressions into smaller sub-expressions to facilitate bug detection and correction. By employing RAG, the LLM is guided using the functional specifications and source code to identify and correct bugs. If the initial guidance through RAG is insufficient, our methodology initiates an iterative bug closure process. This includes incorporating more extensive information from the specifications, fetching additional lines of code for bug localization, and breaking down complex Verilog HDL expressions. In our comprehensive evaluation, we assess the LLM’s capabilities using 9 different categories of bugs. As benchmarks, we use 5 OpenTitan Intellectual Property (IP) cores to demonstrate the scalability and effectiveness of our bug closure methodology where ≈ 60% of the bugs were corrected. Specifically, we evaluate OpenAI’s GPT-4 in its ability to identify and correct functional bugs in Verilog HDL code.